In recent years, there has been a burgeoning interest in multimodal recommender systems within the recommendation systems domain. These systems aim to understand user preferences by leveraging both user interaction data and multimodal information associated with items. This approach frequently results in superior recommendation accuracy compared to traditional models that rely solely on user-item interactions. Despite the advancements of these methods, there is a relatively low utilization of image features in propagating item-item characteristics, an overreliance on text feature similarity, and a frequent neglect of the deep relationships between items, users, and modalities. In response to these challenges, we introduce a novel model termed LLMs-Enhanced Hyper-Knowledge Graph Recommender for Multimodal Recommendation (DOGE). DOGE utilizes large language models (LLMs) to understand image information under the guidance of text information, generating cross-modal features that effectively enhance the relationship between text and image modalities. Subsequently, DOGE constructs a Hyper-Knowledge Graph (HKG) using user-item interaction information and modality features enhanced by large language models. This graph encompasses a wide range of item-item and user-user binary relations and hyper-relations, effectively expanding the feature propagation mechanisms and mitigating the overreliance on text modality. By learning on heterogeneous user-item graphs and homogeneous item-item, user-user graphs, DOGE enhances potential effective propagation between item features and user features, acquiring more effective feature representations of users and items. Comprehensive experimentation across three public real-world datasets illustrates that DOGE attains state-of-the-art (SOTA) performance, exhibiting a 7.2% improvement over the strongest baseline.

Multi-criteria decision making (MCDM) and preference learning (PL) are crucial subfields of intelligent decision-making, both aiming to aid decision-makers (DMs) in selecting, classifying, or ranking alternatives. While MCDM and PL can complement each other to some extent, existing approaches combining MCDM and PL often struggle with large data volumes and complex relational information. To address this, we propose a novel approach called ID-GMLM that integrates graph models and large language models (LLMs) for intelligent decision-making. It reformulates decision-making as a high-parallelism ranking function in the graph domain, using graph neural networks (GNNs) to learn and understand complex relationships between alternatives or criteria, and LLMs to parse and quantify the preferences of DMs. ID-GMLM features a multi-task learning framework that optimizes the primary task of predicting alternative rankings while modeling criterion interactions through the auxiliary task. Additionally, ID-GMLM incorporates a parameter tuning network based on criterion weights and an attention network, allowing the model to adaptively adjust to the context of the current task and the evolving preferences of DMs. Experiments on benchmark datasets demonstrate that ID-GMLM achieves significant performance improvements, inheriting the interpretability and intuitive appeal of MCDM while leveraging the computational efficiency and high accuracy of PL.

Multimodal recommender systems enhance recommendation performance by integrating information from different modalities (e.g., text and images). A common approach is to link items with high modality similarity in modality graphs, helping users explore their interests more broadly. However, existing methods often introduce noise when enhancing modality graphs, making it challenging to effectively balance performance and accuracy. To address this issue, we propose an Interest Tree Augmented Modality Graph RecommendER for Multimodal Recommendation (TAMER). In this framework, we first redistribute item modality features using various component analysis methods to ensure more reliable item similarity within modality graphs. Next, we construct interest graphs based on reliable semantic relationships and prune the interest graphs into multiple interest trees. These interest trees are then applied to the multimodal item-item homogeneous graph to extend potential links within the modality homogeneous graph. The interest tree-based enhancement method effectively captures high-order relationships in the modality graph while avoiding noisy links. The effectiveness of the proposed method is demonstrated through comprehensive experiments on three real-world datasets. Compared with the strongest baseline methods, our method achieves an average improvement of 9.98% across four evaluation metrics. The source code is available at https://github.com/Z-last-ONE/TAMER.

In the era of digital personalization, multi-criteria recommender systems (MCRSs) play a vital role in capturing the multi-dimensional nature of user preferences by considering multiple evaluation criteria rather than relying on a single overall rating. However, existing approaches to MCRSs face challenges in managing graph sparsity, criterion independence, and leveraging semantic information for recommendation tasks. To address these limitations, we propose a novel framework named Large L anguage Models-augmented Light G raph Model for M ulti- c riteria Rec ommendation ( LGMcRec ). LGMcRec integrates the strengths of graph neural networks (GNNs) and large language models (LLMs) to improve the representation and recommendation capabilities of MCRSs. In our model, we construct a tripartite graph structure that captures user-item interactions, item-criterion associations, and criterion interdependencies, effectively addressing issues of sparsity and unmodeled correlations in multi-criteria data. We extend the LightGCN architecture to learn embeddings over this graph, which are further enriched through semantic alignment with embeddings generated by LLMs from textual user and item profiles. To bridge the gap between graph-based and LLM-based embeddings, we employ a contrastive learning approach that maximizes the mutual information between the two embedding spaces, ensuring cohesive and comprehensive user and item representations. Experimental results on three MCRS datasets demonstrate that LGMcRec achieves significant performance improvements over state-of-the-art methods.

Preference learning refers to learning the preferences for a collection of alternatives based on observed or revealed preference information, which are usually represented in the form of an order relation. Some of the existing preference learning methods are parametric in nature, and such methods are faster to train but less expressive. To address this issue, we map the preference learning onto the graph structure, introduce the concept of graph neural networks (GNNs), and propose a two-stage preference learning method based on GNNs, which consists of preference relation prediction stage and object preference ranking stage. The first stage is used to judge the preferences between pairs of objects, the second stage is used to correct the inconsistent preference information of the first stage and rank all objects. In Stage 1, we turn the prediction problem into an edge classification problem on the graph, design a multilayer perceptron (MLP) model to extract edge features, and mine preference information with the help of GNNs. In Stage 2, we construct a comparator neural network structure that takes pairwise preference information as input and generates a score for each object as output. The ranking of the object scores determines the objects’ preference order. Experiments conducted on preference learning datasets have shown that our method achieves significant performance improvements over existing preference learning methods when evaluated in the context of preference service.

Smart grid system can obtain users' daily load data, and by clustering, we can get users' load profiles to divide them into industrial, commercial and residential types. Load data has the characteristic of changing periodically. Within a period, the load profiles are relatively stable. However, load profiles often changes significantly according to reasons like holidays and season changing. When conducting a continuous clustering task for consecutive days, traditional clustering algorithms cannot consider the time-dimension features into analysis, which may make clustering results be very different even if user behaviors are almost the same. Evolutionary clustering (EC) can be taken into consideration. EC doesn't ignore historical clustering results and makes results more stable in a period. However, when dramatic changes happen in user behaviors, the quality of EC's clustering results will decrease significantly. This paper proposed an optimized evolutionary clustering algorithm: Timestamp-Based Self-Adaptive Evolutionary Clustering (TBSAEC). TBSAEC is based on evolutionary clustering, and takes a heuristic approach to pick the evolutionary parameter to maximize the total quality. TBSAEC maintains the stability of continuous-time clustering results while better adapting to changes in user behaviors. Besides, TBSAEC optimize the running efficiency of the algorithm by picking samples in equal portions from historical data instead of the whole data. We applied TBSAEC to the load data of a certain region in east China in 2015, and the results showed that TBSAEC is 3% to 9% higher than the ordinary evolutionary clustering algorithm in total quality, and 87% faster in running time.

Given the problem that the relations among alternatives or criteria cannot be handled well in multicriteria decision making, this paper applies the concept of Pythagorean fuzzy sets to the graph and develops a decision-making approach based on Pythagorean fuzzy graphs (PFGs). First, the weights obtained from the Laplacian energy of PFGs are taken as the subjective weights of criteria. Then, a new Pythagorean fuzzy entropy measure is defined to compute the objective weights of criteria. Meanwhile, a combined weighting method is presented, which makes the criteria weights consist of subjective weights and objective weights. Furthermore, combined weights are applied to the decision-making process and a graph-based Pythagorean fuzzy decision-making method is proposed. Compared with other existing techniques, the proposed approach considers the relations between alternatives and the relations between criteria simultaneously. Finally, an illustrative example is used to verify the approach and demonstrate its effectiveness. The results show that the alternative ranking obtained by the proposed approach is reliable and credible.

The electricity load data recorded by smart meters contain plenty of knowledge that contributes to obtaining load patterns and consumer categories. Generally, the daily load curves are clustered first in order to obtain load patterns of each consumer. However, due to the volume and high dimensions of load curves, existing clustering algorithms are not appropriate in this situation. Thus, a fused load curve clustering algorithm based on wavelet transform (FCCWT) is proposed to solve this problem. The algorithm includes two main phases. First, FCCWT applies multilevel discrete wavelet transform (DWT) to convert the daily load curves for dimensionality reduction. Second, it detects clusters at two outputs of the first phase, and then fuses two groups of clusters with a sub-algorithm named cluster fusion to achieve the optimized clusters. FCCWT is implemented on datasets of both China and United States. Their clustering performances are evaluated by diverse validity indices comparing with four typical clustering methods. The experimental results show that FCCWT outperforms other comparison methods. Additionally, case analysis of two datasets are also provided to discuss the significance of load patterns.

PaaS is known as an application engine which third party developers can deploy their application onto. Security of PaaS becomes important as applications shares resources. How to secure and isolation the resources become an important topic. In this paper, a security PaaS container is proposed which is based on a customized JVM. This container is fully implemented and evaluated in real setting.

An important omission in current development practice for business process (or workflow) management systems is modeling of data & access for a business process, including relationship of the process data and the persistent data in the underlying enterprise database(s). This paper develops and studies a new approach to modeling data for business processes: representing data used by a process as a hierarchically structured business entity with (i) keys, local keys, and update constraints, and (ii) a set of data mapping rules defining exact correspondence between entity data values and values in the enterprise database. This paper makes the following technical contributions: (1) A data mapping language is formulated based on path expressions, and shown to coincide with a subclass of the schema mapping language Clio. (2) Two new notions are formulated: Updatability allows each update on a business entity (or database) to be translated to updates on the database (or resp. business entity), a fundamental requirement for process implementation. Isolation reflects that updates by one process execution do not alter data used by another running process. The property provides an important clue in process design. (3) Decision algorithms for updatability and isolation are presented, and they can be easily adapted for data mappings expressed in the subclass of Clio.

To solve the problem of low information integration for heating industry, a framework of information service platform is proposed. The framework realizes information sharing and integration control of central heating. Four core components are designed to implement process development, service collaboration, publish/subscribe and event rule. A heating management system has been designed based on these components, which realizes intelligent and security of the production management. And three application subsystems have been developed to realize heating maintenance service, multi-level alarm service and heating charging service. The information service platform effectively achieves information integration and rapid service development.

As cloud computing goes, How to provide a security cloud becomes an important problem. Virtual networking plays an important role in cloud computing infrastructure. To identify which node is a critical node become an important research question. In this study, our team analyzed the fat tree network and small-world network, and proposed a network modeling method for the virtual networking. On this basis, we analyze time performance and detecting accuracy of the two critical nodes detecting algorithms. One is based on depth-first search, while the other concentricity analysis.

Cloud computing provide computing resource on demand which helps people to make full use of legacy computing asset. Resource provision is transparent to user but not to provider. Computing provider need to aware of resource utilization. How to monitoring resource utilization become an important problem in cloud computing. In this study, we propose a monitoring architecture and a monitoring model. An Auto window filter algorithm is introduced to help reduce the network traffic.

SUMMARY Modern software often uses ontologies as its key component to store data and their relationships. This is different from using an ontology as a stand‐alone tool for knowledge sharing and representation. The ontology component needs to work with other software components and needs to evolve as the software evolves. Ontology design has been a research topic for years; however, most of these studies focus on using ontologies as stand‐alone applications. This paper studies ontology patterns that can be applied to design ontologies as an integral part of a service‐oriented application. The paper first briefly reviews various ontology design issues including a brief survey of existing ontology design patterns. The paper then outlines general principles for using ontologies in software applications, including the needs to incorporate ontology design process as a part of software development processes, design ontologies as a component of an overall software architecture, and use ontologies to enhance software evolution and the role that ontologies can play in software validation. The paper then proposes some common ontology patterns that can be used to design ontologies in service‐oriented applications. This is followed by examining two international projects, SENSEI and FCINT, where ontologies are used in service‐oriented applications and several ontology design patterns are used. Copyright © 2011 John Wiley & Sons, Ltd.

Due to complex environment of the coal mine, it's necessary to monitor the information of underground environment, device and miner instantly in order to ensure the safety of coal mine production. However, the exiting coal mine can not meet the requirements of coverage without blind spots as it is developed by the wired network. This paper proposes a RESTful Web services mashup augmented coal mine safety monitoring and control automation using ZigBee wireless sensor network, which can collect the underground temperature, humidity methane values and personal position through sensor nodes in the coal mine, and also collects the personnel position information inside the mine, and then implement a RESTful Application Programming Interface (API) on sensor nodes to provide access to sensors and actuators, allowing for them to be easily combined with other enterprise information resources based on the success of mashup applications. We also illustrated three different of scenarios for RESTful Web service mashups representing for coal mine safety monitoring and control automation. Finally, we give the conclusions.

Web2.0 and Cloud Computing are two hot topics in current internet research. PaaS (Platform as a Service) hosts the users' service in an elasticity way and provides the load balance support, but there is not optimizing in load balance for web2.0 application, especially for SNS like website. Load balance optimizing can be divided in two aspects: web cache optimizing and load balance strategy designing. We propose a PUR-SNS (prediction on user requests for social networking services) algorithm for web cache optimizing, which organize web cache based on the social relations. Simulation shows that PUR-SNS can improve caching hit ratio in SNS like environment.

Web Service is one of implementation modes of distributed systems and is becoming the next generation web-based application. As the amount of Web services is increasing, service experience problem is emerging. After Web services are implemented and published, service providers do not know their running effect and user satisfactory degree. So in order to provide better services and improve user experience quality, this paper proposes a three-step service experience approach with feedback to service accessing, in which service experience coefficient is used to measure the running effect of service and user satisfaction degree. At the same time, the test data of the feedback are sent to service providers to create new services or improve services in order to provide better services. With the data of the web site WS-DREAM, the service experience approach to service accessing is designed and implemented. The result indicates that the approach is useful to improve better services.

Web Service is becoming the next generation of web-based application. With enhancement of quality of services and increasing quantity of services, how to recommend the suitable services according to personalized requirement becomes an urgent question. In the existing approaches of service recommendation, the result of service recommendation is the service list in which there is no evaluation standard that we can use to distinguish services with high relevancy and low relevancy. So in real-world, the user may obtain low relative services. To address the problem, in this paper, membership function is analyzed and recommendation measure standard is proposed. With dynamic programming theory, an ontology based approach of service recommendation is provided. In the result of service recommendation, membership as measure index is used to divide high relative services, medium relative services and low relative services. High relative services are recommended to the user. So the recommended services are accurate and available.

Service discovery is the process of finding suitable services and selecting the best alternative for a given task, and service matching algorithm is the kernel technology of the step. Nowadays there are no quantity index of service Query Complete Rate (QCR) and Query Accuracy Rate (QAR), and the efficiency of service matching is low. This paper defines QCR and QAR, and provides a new ontology-based service matching algorithm that uses the theory of query rewriting from key-words to ontology to select suitable services.

Service-oriented modeling is defined as a comprehensive practice encompassing analysis, design and architecture of all organizational software entities. Currently, service-oriented modeling receives more attention in SOA project lifecycle, since SOA application is model-driven and can be fast developed after modeling. However, existing service-oriented modeling approaches reuse only service which is considered to be building blocks of cross-domain applications. This usually falls into the field of OO analysis, just using service to realize function at design and implementation phase, which disobeys the soul of SOA that fast developing roots in extensive reuse. This paper proposes a domain-specific hierarchical ontology system supporting reuse of various service-oriented assets, such as application template, collaboration pattern, workflow and service. The paper also presents a service-oriented modeling process based on the ontology, which accelerates SOA application developing by searching and reusing assets from the first to last modeling phase, instead of starting everything from scratch.

This paper proposes an extensive reuse approach for SOM, which utilizes a multi-facets ontology system supporting reuse of various service-oriented assets, such as business processes, collaboration templates and services. The paper also presents an iterative service-oriented modeling process based on the ontology and assets repository, which reuses assets from the first to last modeling phase by matching between assets descriptions, and results in a service model that represents the specification of the required service-oriented application.