Web APIs have become a fundamental paradigm in the Web 4.0 era, with mashup services emerging as a transformative technology that combines multiple APIs to create comprehensive services. However, existing approaches exhibit two significant limitations: overlooking the quality and completeness of recommendation contexts of new mashup requirements, and failing to effectively extract high-quality collaborative features from multi-perspective service relationships. To address these limitations, we propose LMSR, a novelLLM-enhancedMulti-perspectiveService Feature Learning framework for Web APIRecommendation. LMSR first leverages general-purpose LLM to refine and encode the original requirement descriptions, and employs a Mixture of Service Experts (MoSE)-based context prediction model to precisely predict service information relevant to new requirements, establishing a comprehensive and high-quality recommendation context for mashup requirements. Furthermore, by integrating the predicted recommendation contexts into the LLM through fine-tuning, LMSR effectively extracts collaborative features from multi-perspective service relationships between mashup requirements and APIs, ultimately achieving precise Web API recommendation. Comprehensive experiments on real-world datasets demonstrate that LMSR significantly outperforms 11 baseline approaches across precision, recall, F1-score, and NDCG, validating its effectiveness in Web API recommendation. The codes are available athttps://scdm-shu.github.io/codes/LMSR.zip.

While Large Language Models (LLMs) show promise for Text-Attributed Graphs (TAGs) learning, their deployment is hindered by computational demands. Graph Neural Networks (GNNs) are efficient but struggle with TAGs' complex semantics. We propose LinguGKD, a novel LLM-to-GNN knowledge distillation framework that enables transferring both local semantic details and global structural information from LLMs to GNNs. First, it introduces TAG-oriented instruction tuning, enhancing LLMs with graph-specific knowledge through carefully designed prompts. Next, it develops a layer-adaptive multi-scale contrastive distillation strategy aligning LLM and GNN features at multiple granularities, from node-level to graph-level. Finally, the distilled GNNs combine the semantic richness of LLMs with the computational efficiency of traditional GNNs. Experiments demonstrate that LinguGKD outperforms existing graph distillation frameworks, the distilled simple GNNs achieve comparable or superior performance to more complex GNNs and teacher LLMs, while maintaining computational efficiency. This work bridges the gap between LLMs and GNNs, facilitating advanced graph learning in resource-constrained environments and providing a framework to leverage ongoing LLM advancements for GNN improvement.

Edge service deployment has attracted significant attention in recent years, aiming to optimize service placement on edge servers while satisfying diverse requirements. However, existing approaches often overlook the influence of geographical contexts on service demands, where user needs vary significantly across regions with distinct characteristics. They also fail to account for differences between direct responses and multi-hop forwarding in edge network topology, leading to unsatisfactory edge service deployment strategies. To this end, we formulate the Points of Interest-Based Edge Service Deployment (POI-ESD) problem with topology-aware optimization, integrating POI attributes and spatial distributions while incorporating edge network topology to enhance service placement. By proving the$\mathcal{N}P$-hardness of POI-ESD problem, we propose a novel graph-encoded genetic algorithm, MTGA, to efficiently generate high-quality deployment strategies. It ensures strategic placement of edge services in regions that best match user demands, improving the service utilization and satisfiability for edge users. Extensive experiments on a real-world dataset combining Shanghai Telecom and Baidu Maps POI data demonstrate that MTGA significantly outperforms existing competing approaches, achieving superior performance of edge service deployment.

Connected vehicles due to the high mobility and dynamic network topologies of connected vehicles require accurate QoS that includes high throughput and low latency to assess satisfactory QoE. Existing methods mainly focus on centralized QoS prediction while paying little attention to distributed mobile QoS prediction, making it challenging to protect user privacy information when invoking Web services. Moreover, even though some advanced centralized methods can be transformed into federated architectures, they often face difficulty in capturing latent feature representations of users and services and learning personalized prediction layers between them due to the heterogeneity of the QoS dataset. To address the above issues, we propose a novel framework for distributed QoS prediction, called Combining Personalized Federated Hypernetworks and Shared Residual Learning for Distributed QoS Prediction (FHR-DQP) . FHR-DQP adopts the federated averaging (FedAvg) to aggregate location-aware residual shared feature information across all clients. Additionally, a hypernetwork is leveraged to generate personalized networks for user-service QoS prediction in each client. These components are integrated as a hybrid framework that performs training using a federated approach and makes personalized QoS predictions within each client. Extensive experiments are conducted on a real-world benchmark QoS dataset called WS-DREAM, containing nearly 2,000,000 historical QoS invocation records. Compared with both centralized and federated competing baselines, the results demonstrate that FHR-DQP achieves the highest performance for distributed QoS prediction, when it provides privacy-preserving of users’ QoS invocations.

Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slice. Moreover, the prevalent use of RNNs for modeling temporal feature evolution patterns is constrained by their inherent difficulty in managing long-range dependencies, thereby limiting the detection of long-term QoS trends across multiple time slices. These shortcomings dramatically degrade the performance of temporal QoS prediction. To address the two issues, we propose a novel Graph Attention Collaborative Learning (GACL) framework for temporal QoS prediction. Building on a dynamic user-service invocation graph to comprehensively model historical interactions, it designs a target-prompt graph attention network to extract deep latent features of users and services at each time slice, considering implicit target-neighboring collaborative relationships and historical QoS values. Additionally, a multi-layer Transformer encoder is introduced to uncover temporal feature evolution patterns, enhancing temporal QoS prediction. Extensive experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal QoS prediction across multiple evaluation metrics, achieving the improvements of up to 38.80%.

Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Secondly, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework namedPrivacy-EnhancedFederated ExpandedGraphLearning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service invocations.

The research on continuous control in reinforcement learning has been a hot topic in recent years. The Deep Deterministic Policy Gradient (DDPG) algorithm performs well in continuous control tasks. DDPG algorithm uses experience replay mechanism to train the network model, in order to further improve the efficiency of experience replay mechanism in the DDPG algorithm, the cumulative reward is used as the transiton classification basis, a Deep Deterministic Policy Gradient with Episodic Experience Replay (EER-DDPG) algorithm is proposed. First of all, the transitions are stored in the unit of episode, and two replay buffers are introduced respectively to classify the transitions according to the cumulative reward. Then, the quality of policy can be improved in network model training period by random samping of the episodes with large cumulative rewards. In the continuous control tasks, this algorithm is verified by experiments, and compared with DDPG algorithm, Trust Region Policy Optimization (TRPO) algorithm and Proximal Policy Optization (PPO) algorithm. The experimental results show that EER-DDPG algorithm has better performance.

Deep learning offers advantages such as automatically learning high-level features, better generalization capabilities, and end-to-end learning, which help address the shortcomings of traditional image classification methods in feature design, generalization capabilities, and modeling complex relationships. However, the complex structure of deep learning models and their optimization algorithms often lead to inefficiencies. This research article aims to explore a color image classification algorithm based on TensorFlow. By utilizing the TensorFlow deep learning framework, a Convolutional Neural Network (CNN) model is proposed for classifying color images. The deep neural network structure includes multiple convolutional layers, pooling layers, and fully connected layers, and incorporates the Convolutional Block Attention Module (CBAM) attention mechanism. The model is trained using the backpropagation algorithm. In the experiments, a dataset containing color images of different categories is used to train and test the designed CNN model. By adjusting the model's hyperparameters, optimizers, and loss functions, the performance of the model is optimized, and the classification accuracy is evaluated. The experimental results demonstrate that the color image classification algorithm based on TensorFlow designed in this study achieves good classification results on the dataset used. The model exhibits a high accuracy rate on the validation set, proving the effectiveness and feasibility of the algorithm in color image classification tasks.

We discuss a practical implementation and performance evaluation of a desktop cloud environment that enables international corporations to make their data available to their staff members transparently, regardless of the location of the data and the staff members. It has been implemented for the benefit of the European and American multinational retail companies that operate in the Yangtze River Delta region, these companies normally collect large amounts of sensitive data that demand protection. It is meant to be used as an alternative to off-the-shelf solutions that companies cannot import due to restrictions that the Chinese government has currently in force. As such, it must comply with both local and international regulations imposed by the government. Notably, it is expected to comply with the data security and data protection regulations imposed by the Chinese government and at the same time with international regulations such as the GDPR followed by European countries. Our implementation takes advantage of the functions (e.g., deployment of virtual applications, desktop control management, and secure network access) provided by Citrix — a technology that we have selected after evaluating it against other well-known virtualization technologies. We have divided the implementation and discussion of our system into two parts: network virtualization and virtual architecture. In the end, Login VSI is used to test the performance of our system.

With the continuous development of large languaege models (LLM), LLM technology is having a profound impact on the field of education. This paper takes activity theory as the methodological tool, and aims to explore the internal relationship between LLM and educational activities through scene analysis. Firstly, this paper reviewed the development process of LLM and the related concept connotation of activity theory. Then, a conceptual model of LLM in education application based on activity theory was established. In this model, the application scenarios were analyzed from the aspects of teaching, learning, evaluation and research, and the application characteristics were analyzed by examining the elements of subject, community and object. From the perspective of four subsystems of teaching, learning, evaluation and research, this paper analyzes the impact of the application of LLM on educational activities in detail. This paper provided theoretical support and empirical analysis for the application of LLM in the field of education, further deepened the understanding of the relationship between technology and education, and provided useful reference and guidance for the development of education in the future.

Personal credit assessment, as an essential method to gauge an individual's credit status, exerts profound influences on personal economic life, risk control of financial institutions, establishment of social credit system, as well as the stableness of the overall economy. To address the challenges of delayed updates on bank information, malicious node attacks, issue of lacking user trust and data islands, this paper proposes a personal credit rating method based on asynchronous federated learning that is incentivized by copula reputation. This particular method creates a federated learning incentive mechanism based on copula variable dependence relationships that precisely assesses and measures the credibility of each distributed banking individual without acquiring original data, thereby preventing attacks from malicious nodes. Moreover, it puts forth a lightweight asynchronous federated learning mechanism according to each bank's local model's copula reputation, thereby optimizing the selection of participating nodes to reduce the system's overall cost, and settling the dilemma of bank information status update delays. Lastly, numerical results from real data sets indicate that the asynchronous federated learning personal credit assessment scheme based on copula reputation proposed has favorable accuracy, efficiency and enhanced security, efficiently safeguarding data privacy.

Composed image retrieval is designed to more accurately retrieve target images that align with user intentions by using a combination of reference images and descriptive modification texts. However, existing methods primarily focus on designing complex feature fusion networks while neglecting the prevalent issues of noise and inconsistent sample quality in training data, leading to insufficient cross-modal semantic alignment and sample relevance modeling. To address this, we propose an innovative Tripartite Alignment Network (TAN) that introduces a momentum distillation mechanism, leveraging the historical knowledge of a teacher network as additional super-vision to guide the optimization of the student network. During the feature encoder fine-tuning stage, we design response-based knowledge distillation and feature-based knowledge distillation techniques, explicitly strengthening modal alignment through composed-target contrastive learning and implicitly promoting modal fusion via composed-target matching learning. In the combiner training stage, we incorporate a lightweight combiner network and employ a cross-entropy-based matching loss function, encouraging high matching scores for relevant image-text pairs and low scores for irrelevant pairs. Extensive experiments on the FashionIQ and Shoes datasets demonstrate that TAN exhibits superior performance compared to existing state-of-the-art methods, with notable improvements in R@10 of +14.03% and +13.09%, respectively. These results affirm the effectiveness of momentum distillation in multimodal learning. Access the source code at https://github.com/Maserhe/TAN.

Recently, the edge data caching (EDC) problem has received much attention. It aims to appropriately cache data on edge servers. Existing EDC approaches suffer from a series of limitations. First, they often overlook the diverse characteristics of data, including caching costs and latency preferences. In reality, different types of data vary in size and require different storage resources for caching. The impact of specified latency preferences of edge users for different data on the quality of experience should be considered in the EDC problem. Second, the temporal dynamics of edge users’ data requests and distributions have been insufficiently addressed. To overcome these limitations systematically, this paper focuses on the problem of temporal-aware edge data caching with specified latency preference (TEDC). We first formulate the TEDC problem and transform it into an optimization problem with multiple objectives and global constraints and prove its ${\mathcal{N}}{\mathcal{P}}$-hardness. Then, we propose an optimal approach named TEDC-IP to solve this TEDC problem with the Integer Programming technique and a heuristic algorithm named TEDC-A for finding approximate solutions to large-scale TEDC problems efficiently. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of our approach. The results demonstrate that TEDC-IP and TEDC-A significantly outperform state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.

With the surge in Web service users, user profiling has become increasingly prominent in personalized service recommender system. Graph Neural Networks (GNNs) has emerged as a key technology for user feature extraction. However, these methods mostly focus on modeling pairwise interaction relationships by type and overlook the high-order interaction relationships and deep semantic correlations. Moreover, GNNs’ limited receptive fields restrict their capacity to capture user high-order features effectively. To address these issues, we propose a novel framework for advanced user profiling named Heterogeneous Interaction Graph Transformer (HIGT). Firstly, HIGT constructs a weighted heterogeneous interaction graph from historical user-service interactions, using edge types for interaction modes and weights for their frequency. Secondly, it uses a Transformer to extract high-order semantic attribute correlations and enhance global understanding through self-attention, while proposing a structure-enhanced attention mechanism to incorporate the graph structure into the Transformer architecture for extracting high-order interaction features of users. This dual high-order feature learning method provides deeper insight into users’ preferences for Web services. Extensive experiments on two real-world e-commerce service datasets reveal that HIGT brings a significant performance boost compared with competing models for user profiling.

Fashion image retrieval is one of the important services of e-commerce platforms, and it is also the basis of various fashion-related AI applications. Studies have shown that in a multi-modal environment (images + attribute labels), embedding items into specific attribute spaces can support more fine-grained similarity measures, which is especially suitable for fashion retrieval tasks. In this paper, we propose an attention-based attribute-guided similarity learning network (AttnFashion) for fashion image retrieval. The core of this network is an attribute-guided spatial attention module and an attribute-guided channel attention module, which correspond to the mapping between attributes and image regions, and the mapping between attributes and high-level image semantics, respectively. To make these two modules interact deeply, we design a parallel structure that allows them to share attribute embeddings and guide each other to extract specific features, which also helps to reduce the network parameters of the attention modules. An adaptive feature fusion strategy is proposed to synthesise the features extracted by the two modules. Extensive experiments show that the proposed AttnFashion performs better than current competitive networks in the field of fine-grained attribute-based fashion retrieval.

The proliferation of homogeneous web services has necessitated the task of predicting vacant Quality of Service (QoS) for service-oriented downstream tasks. Existing approaches primarily focus on user-service invocations without considering temporal factors, limiting their applicability in QoS fluctuations over time. Moreover, some investigations are conducted to predict temporally missing QoS, which still suffers from two limitations. First, time-aware collaborative filtering (CF) approaches fail to well capture continuous temporal changes, which lowers the performance of time-aware QoS prediction. Second, they have paid less attention to the high sparsity of user-service QoS invocations across sequentially multiple time slices, which affects the calculation of temporal average QoS, thereby further reducing the accuracy of time-aware QoS prediction. To effectively mine the continuous temporal variations and solve the high sparsity of user-service QoS invocations, we propose a novel time-aware QoS prediction approach named Temporal Reinforced Collaborative Filtering (TRCF). We design temporal reinforced RBS and PCC to improve similarity evaluation that leads to better calculation of temporal average QoS and deviation migration for predicting time-aware QoS. We evaluate TRCF on a large-scale real-world temporal dataset WS-DREAM across 64 time slices and the results demonstrate its superior performance in time-aware QoS prediction, both under relatively dense and extremely sparse QoS situations.

QoS prediction plays an important role in service-oriented downstream tasks. However, most of current state-of-the-art QoS prediction approaches suffer from two limitations. First, traditional approaches typically require collection of user-service historical QoS invocations centrally in order to improve QoS prediction accuracy, which poses a threat to user data privacy. Second, although few of the recent approaches take into account data protection when predicting QoS values, they still cannot effectively capture user-service complex nonlinear invocation relationships, significantly influencing the performance of QoS prediction. To address these two issues, we propose a novel framework of data-protected QoS prediction called Federated Residual Ladder Network (FRLN), which ensures user data protection and effectiveness of predicting missing QoS values. It initially leverages our designed Residual Ladder Network (RLN) to extract latent features of users and services from both low and high dimensional spaces. Then, local QoS prediction models are collaboratively trained by personalized federated learning with the consideration of data heterogeneity. Extensive experiments have been conducted on a real-world large-scale dataset called WS-DREAM, which consists of 5825 Web services from 74 regions and 339 users from 31 regions comprising a total number of 1,974,675 user-service QoS invocations. Experimental results demonstrate the effectiveness of FRLN in multiple evaluation metrics. While the proposed FRLN framework marks a significant step forward for QoS prediction in machine learning, ongoing advancements in ML techniques and expanded datasets are essential for further enhancing its precision and applicability in real-world scenarios.

Fine-Grained Medical Image Classification (FGMIC) aims to identify disease subclasses within the corresponding metaclass. Due to insufficient labeled images and confusing image samples, the accuracy of FGMIC is limited and the trustworthiness of the model is also affected. In this paper, we utilize evidence theory to measure prediction uncertainty and improve the trustworthiness of FGMIC through multiple evidence fusion. Specifically, we consider FGMIC as a hierarchical classification process. At each layer, we construct an evidential classifier to extract classification evidence. Evidence extracted from all layers forms multi-grained evidence. Then, multi-grained evidence are fused through the Dirichlet hyper-PDF, so that evidence of coarse-grained layer classes can be used to enhance the corresponding evidence of fine-grained layer classes. Moreover, the scanned 3D medical image of a patient can generally be divided into three 2D views, with different views containing different features and uncertainties of the pathological region. Inspired by this, the evidential classifier of each layer is split into three sub-evidential classifiers, where one sub-evidential classifier is built on a view. Then, classification evidence from different views is fused using uncertainty-weighted fusion. Experiments on two cancer subtype classification tasks validate that multiple evidence fusion can not only improve prediction accuracy, but also reduce uncertainty and improve the trustworthiness.

In service recommender system, graph neural networks (GNNs) perform message passing through diffusion mechanism based on user-service relationship graph. However, existing GNN-based service recommendation models suffer from two limitations: ❨1❩ message passing is only carried out at firstorder neighbors, as higher-order may cause over-smoothing phenomenon, confining feature propagation in GNNs; and ❨2❩ due to sparse and noisy interactions, the distribution of embedding vectors is nonuniform in the latent space, resulting in unsatisfactory performance for downstream applications. To this end, we propose a fixed global graph diffusion view that is independent of the original user-service observed local view to form a multi-view learning by building contrastive learning (CL) relationship, named as Multi-View Graph Contrastive Learning (MVGCL). Specifically, it enhances the capability of message passing through constructed local and global multi-view graphs, and alleviates the sparse and noisy influences by performing intra-CL within local/global view and inter-CL between multi-view to obtain a more uniform distribution of user and service node representations. Extensive experiments are conducted on three benchmark datasets within different scales, and the results demonstrate that our proposed MVGCL can remarkably outperforms state-of-the-art competing baselines on various evaluation metrics.

Learning the similarity between fashion items is essential for many fashion-related tasks. Most methods based on global or local image similarity cannot meet the fine-grained retrieval requirements related to attributes. We are the first to clearly distinguish the concepts of attribute name and their values and divide fashion retrieval tasks that combine images and text into: attribute-guided retrieval and attribute-manipulated retrieval. We propose a hierarchical attribute-aware embedding network (HAEN) that takes images and attributes as input, learns multiple attribute-specific embedding spaces, and measures fine-grained similarity in the corresponding spaces. It can accurately map different attributes to the corresponding areas of the image, thereby facilitating the feature fusion of two different modalities of text and image, including enhancement and replacement. Then on this basis, we propose three attribute-manipulated similarity learning methods, HAEN_Avg, HAEN_Rec, and HAEN_Cmb. With comprehensive validation on two real-world fashion datasets, we demonstrate that our methods can effectively leverage semantic knowledge to improve image retrieval performance, including attribute-guided and attribute-manipulated retrieval tasks.

With the overwhelming explosion of Web services, how to effectively predict unknown QoS has become a key issue of differentiating large-scale similar or functionally equivalent Web services. However, current state-of-the-art QoS prediction approaches based on deep learning still suffer from two deficiencies. First, they mainly focus on predicting vacant QoS in a centralized manner and scarcely take into account distributed QoS prediction, which makes difficult to protect the privacy information of users invoking Web services. Second, they have ignored the hierarchical collaborative relationship to better extract latent features of users and services, reducing the accuracy of QoS prediction. To address these two issues, we propose a novel framework calledFederatedHierarchicalClustering forDistributedQoSPrediction(FHC-DQP). It collaboratively performs distributed federated training on independent users’ QoS invocations, and then the extracted federated users’ private features are fed to clustering algorithm for partitioning them into a set of clusters. By iteratively federated hierarchical clustering, users are fine-grained partitioned together and those users within the same cluster have stronger collaborative relevance for more effectively learning the latent features of users and services leading to the performance improvement of distributed QoS prediction, where contextual-aware deep neural network is designed for personalized QoS prediction. Extensive experiments are conducted based on a public real-world benchmarking dataset called WS-DREAM with almost 2,000,000 user-service historical QoS invocations. Compared with both centralized and federated competing baselines, the results demonstrate FHC-DQP receives superior performance for distributed QoS prediction, when it provides privacy-preserving of users’ QoS invocations.

Recently, edge user allocation (EUA) problem has received much attentions. It aims to appropriately allocate edge users to their nearby edge servers. Existing EUA approaches suffer from a series of limitations. First, considering users' service requests only as a whole, they neglect the fact that a service request may be partitioned into multiple tasks to be performed by different servers. Second, the impact of the spatial distance between edge users and servers on users' quality of experience is not properly considered. Third, the temporal dynamics of users' service requests has not been fully investigated. To overcome these limitations systematically, this paper focuses on the problem of spatio-temporal edge user allocation with task decomposition (ST-EUA). We first formulate the ST-EUA problem. Then, we transform ST-EUA problem as a multi-objective optimization problem and prove its NP-hardness. To tackle the ST-EUA problem effectively and efficiently, we propose a novel genetic algorithm-based heuristic approach GA-ST, aiming to maximize usersoverall QoE while minimizing migration cost in different time slots. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of GA-ST. The results demonstrate that GA-ST significantly outperforms state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.

How to accurately predict vacant QoS has become a fundamental issue for service-oriented downstream tasks. However, most QoS prediction approaches based on model learning fail to discriminatively capture the latent feature representations of a user and a service, since they either leverage the shallow neural network such as MLP or take advantage of insufficient location information. Moreover, collaborative relationships of similar neighborhood have not been fully taken into account together with prediction model learning. To address these issues, we propose a novel framework for adaptive QoS prediction named Neighborhood-based Collaborative Residual Learning (NCRL). Location-aware two-tower deep residual network is designed to achieve neural QoS prediction by extracting latent features of users and services, which are fed to generate similar neighborhood for collaborative prediction based on historical QoS invocations. They are integrally combined to perform adaptive QoS prediction. Extensive experiments are conducted based on a large-scale real-world QoS dataset called WS-DREAM with almost 2,000,000 historical QoS invocations. The results indicate that NCRL can remarkably outperform state-of-the-art competing baselines.

As the 5th generation (5G) network develops and rolls out rapidly, user requests can be offloaded to nearby edge servers for processing. This alleviates the pressure on the network backhaul and the remote cloud. Nevertheless, the edge user allocation (EUA) problem, as one of the main research challenges in the 5G era, has become a major obstacle to ensuring users’ Quality of Experience (QoE) in the edge computing environment. Conventional EUA approaches, ranging from static global allocation model to online decision-making model, have ignored the long-term impact of the changes in users’ expectations on user-perceived Quality of Service (QoS). Additionally, most existing approaches have not taken into account the distance between an edge user and an edge server, which impacts the user’s data rate profoundly. In this paper, we tackle these challenges by formulating EUA problem as a spatial-temporal one (ST-EUA), which models distance-aware QoS based on the wireless transmission attenuation and models users’ QoE based on the Expectation Confirmation Theory (ECT). To find an appropriate solution for ST-EUA problem, we develop two fuzzy control-based approaches, namely FC and BFC, for on demand scenarios and batch processing scenarios, respectively. They can balance effectively the user consolidation and server load. We conduct extensive experiments based on two widely-used real-world datasets. The results demonstrate the superiority of our FC and BFC in effectiveness and efficiency over the baselines and state-of-the-art.

With the explosive growth in the number and diversity of Web services, correlative research has been investigated on Web service classification, as it fundamentally promotes advanced service-oriented applications, such as service discovery, selection, composition and recommendation. However, conventional approaches are restricted to indiscriminatingly classify Web services, which can trigger many challenges. First, they have not made full advantage of the implicit relationships among multi-dimensional information of Web services, such as the increasing number of service categories. Thus, it leads to low effectiveness of learning and representing service features, failing to ensure the overall accuracy of service classification. Second, the imbalance of service distributions has been ignored, while it is observed that service categories reveal distinct long-tail characteristics. That results in low accuracy on service classification for those categories that contain fewer Web services. To handle the challenges of more effectively learning implicit service features across the service repository, and with a particular concentration on those tail categories that contain fewer Web services, we propose a novel framework called DeepLTSC to more accurately perform the task of Web service classification under long-tail distributions. In DeepLTSC, we first present an improved label attentive convolutional deep neural network (LACNN) with service categories, which can generate deep service features to improve the overall classification performance. Then, a proposed service feature augmentation model (SFA) together with focal loss function is integrated into DeepLTSC to further optimize service features, aiming to boost the classification accuracy on tail service categories. Extensive experiments are conducted on three large-scale real-world services datasets with different long-tail distributions. The results demonstrate that DeepLTSC significantly outperforms state-of-the-art approaches for Web service classification on both overall and tail categories.

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

Sales forecasting is an important part of e-commerce and is critical to smart business decisions. The traditional forecasting methods mainly focus on building a forecasting model, training the model through historical data, and then using it to forecast future sales. Such methods are feasible and effective for the products with rich historical data while they are not performing as well for the newly listed products with little or no historical data. In this paper, with the idea of collaborative filtering, a similarity-based sales forecasting (S-SF) method is proposed. The implementation framework of S-SF includes three modules in order. The similarity module is responsible for generating top-k similar products of a given new product. We calculate the similarity based on two data types: time series data of sales and text data such as product attributes. In the learning module, we propose an attention-based ConvLSTM model which we called AttConvLSTM, and optimize its loss function with the convex function information entropy. Then AttConvLSTM is integrated with Facebook Prophet model to forecast top-k similar products sales based on their historical data. The prediction results of all top-k similar products will be fused in the forecasting module through operations of alignment and scaling to forecast the target products sales. The experimental results show that the proposed S-SF method can simultaneously adapt to the sales forecasting of mature products and new products, which shows excellent diversity, and the forecasting idea based on similar products improves the accuracy of sales forecasting.

Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model.

Nowadays, the world is witnessing a rapid development of edge computing. As an important issue in the edge computing paradigm, the edge user allocation (EUA) problem has attracted considerable attention. EUA aims at allocating the end-users in a specific area to the edge servers in that area, and ensure end-users' low-latency access to app vendor's services deployed on those edge servers. However, existing approaches simply assume that each edge server has a specific coverage and neglect the complexity of wireless signal transmission. To ensure end-users' low latency, an EUA approach must take into account the distance between end-users and their nearby edge servers, as it significantly impacts their Quality of Experience (QoE). Accordingly, EUA must maximize the overall QoE of the app vendor's users. To tackle this new distance-aware EUA problem, we propose two novel approaches, namely DEUA-O and DEUA-H. DEUA-O aims to find the optimal solution while DEUA-H aims to find the sub-optimal solution in large-scale scenarios efficiently. Four series of experiments are conducted on a real-world dataset to evaluate DEUA-O and DEUA-H. The results demonstrate the substantial gains of our approaches over the state-of-the-art.

Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called Neighborhood-integrated Deep Matrix Factorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.

Correlative approaches have attempted to cluster web services based on either the explicit information contained in service descriptions or functionality semantic features extracted by probabilistic topic models. However, the implicit contextual information of service descriptions is ignored and has yet to be properly explored and leveraged. To this end, we propose a novel framework with deep neural network, called DeepWSC, which combines the advantages of recurrent neural network and convolutional neural network to cluster web services through automatic feature extraction. The experimental results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

QoS-aware Web service composition has recently become one of the most challenging research issues. Although much work has been investigated to solve the problem, they mainly focus on certain QoS of Web services, while QoS with uncertainty exposes the most important characteristic in a real and highly dynamic environment on the Internet. In this paper, with the consideration of uncertain service QoS, we model the issue of Web service composition with QoS uncertainty that is translated into a multi-objective optimization problem via uncertain interval number, which can be solved by our proposed approach via an non-deterministic multi-objective evolutionary algorithm using the strategy of decomposition. Large-scale empirical experiments have been conducted on our simulated datasets. The experimental results demonstrate that our proposed approach can effectively and efficiently find an optimum composite service solution set with satisfactory convergence.

In this paper, we consider a class of constrained nonconvex regularized minimization problems, where the constraints is linearly constrained. It was reported in the literature that nonconvex regularization usually yields a solution with more desirable sparse structural properties beyond convex ones. However, it is not easy to obtain the proximal mapping associated with nonconvex regularization, due to the imposed linearly constraints. In this paper, the optimization problem with linear constraints is solved by the Linearized Alternating Direction Method of Multipliers (LADMM). Moreover, we present a detailed convergence analysis of the LADMM algorithm for solving nonconvex compositely regularized optimization with a large class of nonconvex penalties. Experimental results on several real-world datasets validate the efficacy of the proposed algorithm.

Multi-class classification problem is still a research hotspot in machine learning, and researchers dedicate themselves to create new algorithms with higher efficiency and accuracy. A Modularity-based Hierarchical Classification Tree (MHCT) is proposed in this paper, which derived from the idea of community detection, and the structure of the tree is similar to the hierarchical cluster tree. This classification approach is a supervised learning method combined with modularity for its convergence indicator. After the building process of the tree from bottom to top, several classifier predictors are created in the training step. Finally, the comparison experiments are conducted between our methods and other two kinds of popular multi-class classification algorithms (i.e. support vector machine and decision tree), using ten benchmark datasets from UCI (University of California, Irvine) machine learning repository. In this work, the experimental results indicate that the proposed methods have drastically reduced the excessive training time while maintaining accuracy is comparable to the other algorithms.

QoS values may significantly vary due to the invocation of Web services under dynamical network environment. Although some of approaches apply uncertain QoS to computing the skyline for the reduction of the number of candidate services, they have no uncertain QoS model that needs to be aligned to realistic invocation and execution of Web services. To solve the issue, this paper presents a novel approach to uncertain service skyline via Chebyshev's inequality and interval number. We model uncertain QoS of a Web service by a QoS matrix and then each dimension is shrank to an uncertain QoS scope by interval number. Finally, based on the QoS model, we propose an uncertain service skyline algorithm to compare the QoS of two Web services with domination relationship strategy. Extensive experiments have been conducted on 1,558,224 Web service invocation records. The experimental results demonstrate the effectiveness of our proposed approach.

In this demo, we propose the self-trustworthy and secure Internet protocol (T-IP) for authenticated and encrypted network layer communications. T-IP has the following advantages: 1) Self-Trustworthy IP address. 2) Low connection latency and transmission overhead. 3) Reserving to be stateless (an important merit of IP). 4) Compatible with the existing TCP/IP architecture. We have implemented the protocol and deployed it in our campus network. Compared with IPsec, the evaluation shows that T-IP has a much lower transmission overhead and connection latency.

Online social networks have found a significant increase in their popularity in recent years. All the networks have community structure, and one of the research problems mostly frequently tackled is the discovery of communities. An overlapping community is a network structure that allows one node to be a member of multiple communities. The method presented in this paper aims at detecting overlapping communities in social networks, and its novelty lies in that it combines with the Microblog User Model (MUM) which can reflect the interest of the user accurately. First, the MUM network, which is an undirected and weighted network, is constructed by computing the similarity among MUMs. Afterwords, Overlapping Community Detection based on MUM (OCD-MUM) is performed to partition the network. A community stops expanding when the fitness function reaches a local maximum. The communities detected are locally optimized. A user's interest is not only decided by the MUM, but it is also affected by the communities the user belongs to. The community model can reflect the interest of the community. The MUM is updated with community model of its communities, and therefore the interest of the user can be predicted by these communities. Our experiment result shows that OCD-MUM has a higher modularity Q value than traditional methods and the predicted interest is more close to the real world situations.

With the information overload in web services, micro-blog has been increasingly providing as a media for end-users to express their opinions. The notable feature of micro-blog articles is prone to be a burst of popularity during a short period. In addition, diverse interests make users bored in redundant items in most recommender systems. Therefore, providing users with diverse popular micro-blogs that suit their interesting topics is an important issue. In this paper, depending on forwarding number and comment number of micro-blogs, an effective model for popularity prediction is proposed to discover popular topics. Then, a MaxMin diversity algorithm based on content distance and popularity density is proposed to discover top k micro-blogs. Finally, we design a diverse personalized popularity attention (DPPA) recommendation approach for target user. We conduct extensive experiments on large scale micro-blog datasets. The experimental results show that our proposed approach can satisfy user's requirements with a higher recall than personal attention methods.

BGP prefix hijacking is one of the main threatens for the Internet. It is important to identify the impact factors for prefix hijacking. This paper studies the problem from the view of AS logical topology by analysis of the data from the snapshots of CAIDA. We propose a hierarchical model based on AS relationship to classify the AS nodes into different level and define core size of each node to prioritize them in each level. Two metrics named infected number and infected diameter are introduced to analyze the relationship between the logical structural characters of AS node and the impact of prefix hijacking. The results show that core size, which reflects the relation of an AS node with Tier-1 AS nodes, and AS level are two main important factors. AS nodes in higher level or with bigger core size are able to infect more nodes. However, AS node in lower level has longer infected diameters. This phenomenon indicates that the prefix hijacking with attacker in lower level is harder to detect.

Electrodes connection on the scalp needs to apply gel or paste on the scalp and fit EEG-cap on the head and this procedure also needs to deal with the hair on it. By comparison, to fit EEG electrodes on the forehead area is much easier because there is no hair on it. If correlations of the EEGs generated from the forehead area are high with respect to EEGs from the sensorimotor area, it is then possible to achieve relatively high classification accuracy in motor imagery tasks just using EEG from forehead channels. In this way, it will help to make the procedure of motor imagery tasks much easier and convenient. Because correlation coefficients is often used to measure the similarity of two signals, it is necessary to study whether there is a high correlation between forehead channels' EEG and EEG from sensorimotor area during MI(motor imagery) tasks. In this paper, EEG data from three subjects were used in the tests. Firstly, a test was conducted on the correlation between EEGs from forehead 8(Fp1-Af8) channels and EEGs from the sensorimotor area channel C3, C4 during the MI tasks. The correlation is calculated with respect to ERP (event-related-potential), spectral power between 6-25 Hz, and ERSP (event-related-spectral-perturbation) at Alpha rhythms 8-12 Hz. The results of the correlation tests are mostly above 70%. In particular, for subject Sl1, the correlation coefficient of ERSP between forehead channels and C3, C4 are as high as 0.9 during the left hand movement imagery trials. Secondly, we did a test on the classification of imagined left/right hand movement tasks using EEGs from 8 electrodes in the forehead. Classification results show that the accuracy of the forehead 8 channels' EEGs are as high as 81% for subject Sk6 comparing to 90% using 29 channels' EEG signal neighboring to C3, C4. For subject Sk3 and subject Sl1, the accuracies are 65% and 79% comparing to 80% and 83% using EEG signals from 29 channels neighboring to C3, C4. So there are high correlation between EEGs from the forehead area and EEGs from the sensorimotor area. That is to say, we can use EEGs from forehead in some situations, such as classification of left/right imagery, because they are much easier to measure than EEG form the sensorimotor area. This will make BCI system more portable and more convenient to use.

The concept of brain-computer interface (BCI) has emerged over the last three decades as a promising alternative to the existing interface methods. However the BCI framework generally spoken only emphasizes on the aspects of BCI signal processing, lacking of the function of visualization and virtual reality (VR) feedback. This paper designs a general and extendable framework which has the ability of offline, online analysis, visualization, and VR feedback. For the researchers, they can use it to analyze the online EEG signals, and observe the dynamic brain information of subjects. Meanwhile, the researchers can also do the offline analysis. For subjects, VR technology can provide a more secure and realistic environment for training and tuning neutrally controlled interfaces to real-world devices, such as wheelchairs. At last, the methods and algorithms used in the framework are also described.

Watershed transformation is a common technique for image segmentation. However, its use for medical image segmentation has been limited particularly due to over-segmentation. In response to the characteristics of medical image, especially the contour extraction from the MRI (magnetic resonance imaging) brain image, this paper proposes an improved method in order to overcome the drawbacks. Firstly, multi-scale alternating sequential filtering by reconstruction is introduced to eliminate the noise and simplify the input images, and the loss of boundary information can be avoided. Secondly, two methods of h-minima and minima imposition are imposed on the gradient image to mark the minima regions, so all its local minima are suppressed. Finally, the watershed algorithm is applied to the marked gradient images to get the contour of brain. Experimental results show that the improved method can be applied to contour extraction of MRI brain image with good result, and the mean reduction of local minima in the over-segmented image of regions is 68.26% compared to the watershed transformation based on mark extraction.

A main line of research for introducing explanation capabilities in knowledge-based system supposes that explanations are used to transmit knowledge from the machine to a user and to improve learning ability of the latter. A reasonable explanation should be adapted to different level of users because different users have different knowledge. To describe the difference and then generate a personalized explanation is the main problem. In this paper, a novel method called FUM-CE (fuzzy user model based customized explanation) is proposed, in which a fuzzy user model called FUM is defined, and then several new algorithms are proposed to initialize FUM, update FUM, and extract correlative knowledge for explanation based on the FUM. FUM-CE can provide different and suitable explanation for the different users with different domain knowledge, by which the understandability and acceptability of the expert system for earthquake prediction are improved