Multimodal recommendation is a crucial technology on social media platforms. It is widely applied in scenarios such as product recommendation and advertising delivery. However, existing multimodal recommendation approaches often overlook invariant semantic features that persist across modalities, leading to decreased robustness and generalization. To address this limitation, we proposeGILMRec, a novelgraphinvariantlearning-basedmultimodal social mediarecommendation framework. The GILMRec introduces an invariant feature learning strategy to extract invariant features separately from visual and textual modalities and employs an attention-based fusion mechanism to integrate them into a unified embedding. Specifically, we construct modality-specific similarity graphs and apply top-$t$neighbor aggregation, enhancing the consistency of invariant features while effectively suppressing modality-specific noise. Extensive experiments on three Amazon benchmark datasets and a large-scale dataset [baby, sports, clothing, and compact discs (CDs)] demonstrate that GILMRec consistently outperforms twelve state-of-the-art baselines. The results confirm the efficiency of invariant features in capturing robust multimodal representations and improving recommendation performance, particularly in sparse data scenarios.

Self-supervised heterogeneous graph representation learning (SSHGRL) is a key technique for embedding heterogeneous graphs, enabling effective analysis and modeling of social networks and other graph-structured data, which are central to knowledge discovery and the study of social systems. However, existing SSHGRL methods are hardly applied to large-scale heterogeneous graph environments due to the normally used metapath decomposing mechanism being graph-size-sensitive. Moreover, the existing self-supervised signals are normally created from Shared Mutual Information (SMI) of different graph views that ignore the Non-SMI (NMI) contained in the same view. This results in the model tending to learn insufficient graph representation. To this end, this article proposes a designated masking propagation (DMP) mechanism to process heterogeneous graphs without using metapath. Moreover, based on the DMP graph view, a novel sufficient representation is proposed to learn the effective graph representation by combining both NMI and SMI. Extensive experiments on eight large- and medium-scale heterogeneous graph datasets demonstrate the superiority of our method, setting new state-of-the-art performance in various big data contexts.

Distributed Computing Continuum Systems (DCCS) unify cloud, fog, edge, and Internet of Things (IoT) into a single execution fabric. At scale, heterogeneity and bursty arrivals make it hard to meet per task deadlines. Prior approaches based on learning, optimization, market mechanisms, or class level slicing depend on global state or iterative coordination. Decisions lag arrivals, isolation is scoped to coarse classes rather than individual tasks, and the deadline violation ratio (DVR) rises. We present Task-level Adaptive Computing Slicing (TACS), a fine grained slicing paradigm that delivers task aligned resource governance through autonomous shard management. For each arriving task, TACS executes decentralized scheduling at the shard level to instantiate an ephemeral micro-slice governed by an autonomous shard formed exactly by the task's participants. Within the shard, participants apply closed form rules to make local resource allocation decisions. This design achieves strict task level isolation and precise, scalable matching between supply and demand without global coordination or model retraining. In simulations with 100 to 1000 heterogeneous nodes and a range of loads and heterogeneity levels, TACS maintains DVRs below 1% and provides steadier, higher throughput than advanced baselines. Under adverse conditions, representative baselines exceed 20% DVR and in several cases require retraining when device populations change.

Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the latest works use original node features to synthesize a similarity graph that can correct the structure of the noised graph. This idea is based on the homogeneity assumption, which states that similar nodes in the homogeneous graph tend to have direct links in the original graph. However, similar nodes in heterogeneous graphs usually do not have direct links, which can not be used to correct the original noise graph. This causes a significant challenge in noised heterogeneous graph learning. To this end, this paper proposes a novel synthesized similarity-based graph neural network compatible with noised heterogeneous graph learning. First, we calculate the original feature similarities of all nodes to synthesize a similarity-based high-order graph. Second, we propose a similarity-aware encoder to embed original and synthesized graphs with shared parameters. Then, instead of graph-to-graph supervising, we synchronously supervise the original and synthesized graph embeddings to predict the same labels. Meanwhile, a target-based graph extracted from the synthesized graph contrasts the structure of the metapath-based graph extracted from the original graph to learn the mutual information. Extensive experiments in numerous real-world datasets show the proposed method achieves state-of-the-art records in the noised heterogeneous graph learning tasks. In highlights, +5~6\% improvements are observed in several noised datasets compared with previous SOTA methods.

The Minimum Dominating Set problem (MDS) is a challenging NP-Hard problem with many practical applications. In this paper, we focus on its generalization, the Minimum Weight Dominating Set problem (MWDS). We first propose a novel lower bound for MWDS and prove a condition in which the computed lower bound is tight, then present a new local search approach, called Dual Bounds Search (DBS), which searches for a lower bound and an upper bound simultaneously in an alternate and collaborative way. We implement the lower bound algorithm and integrate two state-of-the-art local search algorithms into our DBS approach to obtain two new DBS algorithms for MWDS. Extensive experiments show that DBS approach can improve the local search performance for MWDS significantly. Thanks to the lower bound, new DBS algorithms can also provide a quality measure of solutions, which is practical in applications and is a clear difference from existing local searches for MWDS.

Anomalies often occur in real-world information networks/graphs, such as malevolent users, malicious comments, banned users, and fake news in social graphs. The latest graph anomaly detection methods use a novel mechanism called truncated affinity maximization (TAM) to detect anomaly nodes without using any label information and achieve impressive results. TAM maximizes the affinities among the normal nodes while truncating the affinities of the anomalous nodes to identify the anomalies. However, existing TAM-based methods truncate suspicious nodes according to a rigid threshold that ignores the specificity and high-order affinities of different nodes. This inevitably causes inefficient truncations from both normal and anomalous nodes, limiting the effectiveness of anomaly detection. To this end, this paper proposes a novel truncation model combining contextual and global affinity to truncate the anomalous nodes. The core idea of the work is to use contextual truncation to decrease the affinity of anomalous nodes, while global truncation increases the affinity of normal nodes. Extensive experiments on massive real-world datasets show that our method surpasses peer methods in most graph anomaly detection tasks. In highlights, compared with previous state-of-the-art methods, the proposed method has +15% ~ +20% improvements in two famous real-world datasets, Amazon and YelpChi. Notably, our method works well in large datasets, Amazin-all and YelpChi-all, and achieves the best results, while most previous models cannot complete the tasks.

Arrhythmia diagnosis based on electrocardiogram (ECG) signals is crucial for early screening of cardiovascular diseases and long-term health monitoring, particularly in home and wearable device scenarios. However, due to significant inter-individual differences in ECG signal morphology, the performance of the model drops sharply when applied to unseen individuals. Moreover, short-term monitoring is difficult to capture nocturnal or occasional abnormalities. To overcome these limitations, a cloud-edge-end medical multimedia collaborative auxiliary diagnosis system was studied and constructed. Specifically, we propose an arrhythmia classification algorithm based on unsupervised domain adaptation, which effectively improves the classification accuracy in inter-patient scenarios. In addition, ECG signals are collected using self-developed portable home heart rate monitoring devices, and real-time analysis is conducted by algorithms deployed on mobile devices to generate visual reports. Doctors review them in the cloud, thus establishing a complete closed-loop collaborative auxiliary diagnosis and treatment process. Experimental results show that the proposed algorithm achieves an accuracy of 97.83% on the MITDB dataset, significantly exceeding the existing state-of-the-art methods. After 15 days of testing with 100 volunteers, the system demonstrated a user satisfaction score of 4.7/5.0, stable Bluetooth connectivity, and a battery life of up to 29 hours.

Generalist Graph Anomaly Detection (GGAD) extends traditional Graph Anomaly Detection (GAD) from one-for-one to one-for-all scenarios, posing significant challenges due to Feature Space Shift (FSS) and Graph Structure Shift (GSS). This paper first formalizes these challenges and proposes quantitative metrics to measure their severity. To tackle FSS, we develop an anomaly-driven graph invariant learning module that learns domain-invariant node representations. To address GSS, a novel structure-insensitive affinity learning module is introduced, capturing cross-domain structural correspondences via affinity-based features. Our unified framework, IA-GGAD, integrates these modules, enabling anomaly prediction on unseen graphs without target-domain retraining or fine-tuning. Extensive experiments on benchmark datasets from varied domains demonstrate IA-GGAD’s superior performance, significantly outperforming state-of-the-art methods (e.g., achieving up to +12.28\% AUROC over ARC on ACM). Ablation studies further confirm the effectiveness of each proposed module. The code is available at \url{https://github.com/kg-cc/IA-GGAD/}.

Multimodal recommendation has become a key technology for social media platforms. It is widely used in content recommendation, user preference analysis, advertisement placement, etc. Existing recommendation methods mainly focus on learning multimodal embeddings from direct interactions between users and items, ignoring indirect interactions among users-to-users and items-to-items. This limits the further exploration of potential interests between users and items. Moreover, during the model training, classical recommendation methods usually randomly select uninteracted items of a user as their negative samples. This may introduce significant learning bias, as uninteracted items could be false negatives and still potentially interest the user. To this end, we propose a novel indirect interactions discovery and true negative sampling multimodal recommendation (ITMRec) method to further explore potential user interests and mitigate the issue of false negative samples during learning. Specifically, we propose an indirect interactions discovering (IID) model to explore the latent interests among users-to-users and items-to-items. Then, we propose a true negative sampling (TNS) model to refine negative sampling that can alleviate the false negative sample problem. Finally, we enhance existing collaborative filtering methods by integrating representations derived from multimodal content, indirect interactions discovery, and refined negative sampling strategies, allowing for more precise alignment with users’ latent interests. Extensive experiments on three benchmark datasets demonstrate that our ITMRec significantly outperforms state-of-the-art recommendation baselines, achieving a 3.64% improvement over peer methods. The code is available athttps://github.com/long-best/ITMRec.git.

Self-supervised Heterogeneous Graph Representation (SSHGRL) learning is widely used in data mining. The latest SSHGRL methods normally use metapaths to describe the heterogeneous information (multiple relations and node types) to learn the heterogeneous graph representation and achieve impressive results. However, establishing metapaths requires lofty computational costs that are too high for the medium and large graphs. To this end, this paper proposes a Reserving-Masking-Reconstruction (RMR) model that can fully consider heterogeneous information without relying on the metapaths. In detail, we propose a reserving method to reserve to-be-masked nodes' (target nodes) information before graph masking. Second, we split the reserved graph into relation subgraphs according to the type of relations that require much less computational overheads than metapath. Then, the target nodes in each relation subgraph are randomly masked with minimal topology information loss. After, a novel reconstruction method is proposed to reconstruct the masked nodes on different relation subgraphs to establish the self-supervised signal. The proposed method requires low computational complexity and can establish a self-supervised signal without deeply changing the graph topology. Experimental results show the proposed method achieves state-of-the-art records on medium and large-scale heterogeneous graphs and competitive records on small-scale heterogeneous graphs. The code is available at https://github.com/DuanhaoranCC/RMR.

Self-supervised graph representation Learning (SSGRL) is an emerging technique for machine learning-based expert applications. SSGRL can effectively encode unlabeled data into machine-understandable knowledge embeddings that can be used in downstream expert tasks. Recently, Masked-Graph-Model (MGM) has achieved extraordinary performances in the field of SSGRL. However, MGM inevitably suffers from a so-called Negative-Migration (NM) problem on unbalanced datasets for specific tasks such as graph classification. The NM problem forces the MGM to predict (reconstruct) the dominant nodes causing the ignoring of the minority but critical nodes. To this end, a novel online prediction module is proposed to dynamically predict the nodes with extended contexts that enable minority nodes can have more weights in the model. Based on the online prediction module, a graph representation learning model is proposed to alleviate the NM problem. Extensive experiments demonstrate that the model outperforms state-of-the-art methods on unbalanced datasets and achieves competitive performance on balanced datasets. Moreover, extra experiments demonstrate that the proposed method requires less computational resource overhead than existing methods. The code is available at: https://github.com/DuanhaoranCC/SimGOP.

Heterogeneous Information Networks (HINs) are crucial in various intelligent systems. The latest advancements in HIN learning aim to combine meta-paths and hypergraphs, capitalizing on their strengths for further success. However, existing methods typically transform meta-paths into hypergraphs by simply removing the original edges from the meta-paths to integrate two semantics. This will inevitably encounter semantic ambiguity, a so-called semantic-shift problem, during the “meta-path → hyperedges” transforming, causing limited improvements. To address this, we introduce a novel fusion framework that distills knowledge from meta-paths into hypergraphs, mitigating such a problem. Specifically, we propose a unique hyperedge extraction method for constructing the hypergraph, incorporating various aspects instead of relying solely on one type of meta-path. Subsequently, we introduce a shallow student model to capture high-order information from the hypergraph, complementing a teacher model that focuses on encoding low-order information from meta-paths. Then, a distillation framework is employed to integrate explicitly multi-order information into the student. Experimental results across diverse datasets demonstrate a substantial improvement in node classification tasks, with an average accuracy increase of 2.1% over existing state-of-the-art methods.

Abstract Self-supervised graph representation learning has been widely used in many intelligent applications since labeled information can hardly be found in these data environments. Currently, masking and reconstruction-based (MR-based) methods lead the state-of-the-art records in the self-supervised graph representation field. However, existing MR-based methods did not fully consider both the deep-level node and structure information which might decrease the final performance of the graph representation. To this end, this paper proposes a node and edge dual-masked self-supervised graph representation model to consider both node and structure information. First, a dual masking model is proposed to perform node masking and edge masking on the original graph at the same time to generate two masking graphs. Second, a graph encoder is designed to encode the two generated masking graphs. Then, two reconstruction decoders are designed to reconstruct the nodes and edges according to the masking graphs. At last, the reconstructed nodes and edges are compared with the original nodes and edges to calculate the loss values without using the labeled information. The proposed method is validated on a total of 14 datasets for graph node classification tasks and graph classification tasks. The experimental results show that the method is effective in self-supervised graph representation. The code is available at: https://github.com/TangPeng0627/Node-and-Edge-Dual-Mask .

Internet-of-Things (IoTs) entity discovery plays an important role in the Industrial IoTs, especially with the rapidly increasing and updating of IoT sensors in the industrial environment driven by the era of Industry 4.0 and intelligent manufacturing. However, large numbers of nonsmart sensors are required in the industrial environment, causing IoT entity discovery challenges. Unlike the smart sensor, the nonsmart sensor with limited computation and communication ability is hard to discover and recognize by traditional IoT platforms. Aiming at the challenge, this work proposes a novel IoT entity discovery middleware for nonsmart sensor discovery in the industrial environment. The proposed middleware combines both sensor knowledge graphs and sensor data values to build an IoT entity discovery and recognition model. A knowledge–data fused learning network is proposed for the model to identify the data type, function, and other information of the nonsmart sensor. At last, a prototype middleware with the discovery and recognition model is produced to implement nonsmart sensor discovery. In the experimental evaluations, the prototype middleware tests various nonsmart sensors and achieves 87.6% recognition accuracy. In real-world case studies, the prototype middleware proves the feasibility and effectiveness of nonsmart sensor discovery in the industrial environment.

Real-Time Traffic Flow Prediction (RT-TFP) is one of the critical technologies for implementing the Intelligent Transportation System (ITS), enabling rapid and accurate prediction of real-time traffic flow at intersections. RT-TFP typically needs to be deployed on-site edge devices for real-time traffic flow calculation that requires low inference latency and minimal computational resources. However, the existing Traffic Flow Prediction (TFP) models are generally based on spatiotemporal graph neural networks (STGNNs), which are complex and require high computational resources and relatively high inference times that can hardly be deployed on edge devices. To this end, this work proposes a simple RT-TFP model, SpatioTemporal-MultiLayer Perceptron (ST-MLP), which requires low computational resources and inference times. The base idea of this work is to establish a spatio-temporal MLP model to replace the STGNN model for conducting the TFP, which is much faster and simpler. Specifically, first, a TempEncoder is proposed to encode the temporal information into the MLP features. Then, a Spatiotemporal Mixer is proposed to mix spatial information into the temporal-enriched MLP features. After, MLP features are distilled from a complex STGNN model to obtain a simple MLP that inherits complete Spatial-Temporal information of the traffic graph. The experimental results on four real-world datasets show the proposed model achieves competitive prediction accuracy with STGNN models in much fewer computational resources and lower prediction time costs. It is worth noting that, the proposed method is faster than the compared STGNNs by an average of 21.62 times (~10.81s$\rightsquigarrow ~\sim 0.50$s). Interestingly, the proposed ST-MLP even has a −3.23% error rate decreasing on average compared to the corresponding STGNN model. Moreover, the error rate of the proposed ST-MLP decreases over pure MLPs by −3.92%$\sim -42.62$%. The source code is available at:https://github.com/zhangjunfeng1234/ST-MLP

Currently, unsupervised industrial anomaly detection and anomaly localization based on deep learning have achieved great success. The most commonly used dataset for industrial anomaly detection and anomaly localization is MV-TAD, and the most commonly used evaluation metric is AUROC. Most research methods are based on the above datasets and experiment evaluation metrics. Although the most advanced method has nearly 100% AUROC index values in the above dataset, the results are still not ideal, as seen by observing the anomaly segmentation maps after the experiments. Therefore, in this paper, we use a new metric, F1-measure, to evaluate the experimental performance of industrial anomaly detection and anomaly localization models. Compared with the AUROC metric, the F1-measure ensures complete and accurate detection by reconciling Precision and Recall. We use ResNet34 and WideRes-Net101 pre-trained encoders based on the current state-of-the-art normalized flow-based generative model anomaly detection method to train and test on the dataset, and achieve good experimental performance. In addition to MVTAD, we extended the dataset to DAGM 2007, BTAD, trained and tested them. Furthermore, we use the new evaluation metric, F1-measure, to evaluate the experimental results.

Zero-shot learning (ZSL) is an important research area in computer-supported cooperative work in design, especially in the field of visual collaborative computing. ZSL normally uses transferable semantic features to represent the visual features to predict unseen classes without training the unseen samples. Existing ZSL models have attempted to learn region features in a single image, while the discriminative attribute localization of visual features is typically neglected. To handle the mentioned problem, we propose a pre-trained Masked Autoencoders(MAE) based Zero-Shot Learning model. It uses multi-head self-attention in Transformer blocks to capture the most discriminative local features from a partial perspective by considering both positional and contextual information of the entire sequence of patches, which is consistent with the human attention mechanism when recognizing objects. Further, it uses a Multilayer Perceptron(MLP) to map visual features to the semantic space for relating visual and semantic attributes, and predicts the semantic information, which is used to find out the class label during inference. Both quantitative and qualitative experimental results on three popular ZSL benchmarks show the proposed method achieves the new state-of-the-art in the field of generalized zero-shot learning and conventional zero-shot learning. The source code of the proposed method is available at https://github.com/yangjingqi99/MAE-ZSL

Graph representation is a critical technology in the field of knowledge engineering and knowledge-based applications since most knowledge bases are represented in the graph structure. Nowadays, contrastive learning has become a prominent way for graph representation by contrasting positive-positive and positive-negative node pairs between two augmentation graphs. It has achieved new state-of-the-art in the field of self-supervised graph representation. However, existing contrastive graph representation methods mainly focus on modifying (normally removing some edges/nodes) the original graph structure to generate the augmentation graph for the contrastive. It inevitably changes the original graph structures, meaning the generated augmentation graph is no longer equivalent to the original graph. This harms the performance of the representation in many structure-sensitive graphs such as protein graphs, chemical graphs, molecular graphs, etc. Moreover, there is only one positive-positive node pair but relatively massive positive-negative node pairs in the self-supervised graph contrastive learning. This can lead to the same class, or very similar samples are considered negative samples. To this end, in this work, we propose a Virtual Masking Augmentation (VMA) to generate an augmentation graph without changing any structures from the original graph. Meanwhile, a node augmentation method is proposed to augment the positive node pairs by discovering the most similar nodes in the same graph. Then, two different augmentation graphs are generated and put into a contrastive learning model to learn the graph representation. Extensive experiments on massive datasets demonstrate that our method achieves new state-of-the-art results on self-supervised graph representation. The source code of the proposed method is available at https://github.com/DuanhaoranCC/CGRA.

Zero-shot learning (ZSL) aims to predict unseen classes without using samples of these classes in model training. The ZSL has been widely used in many knowledge-based models and applications to predict various parameters, including categories, subjects, and anomalies, in different domains. Nonetheless, most existing ZSL methods require the pre-defined semantics or attributes of particular data environments. Therefore, these methods are difficult to be applied to general data environments, such as ImageNet and other real-world datasets and applications. Recent research has tried to use open knowledge to enhance the ZSL methods to adapt it to an open data environment. However, the performance of these methods is relatively low, namely the accuracy is normally below 10%, which is due to the inadequate semantics that can be used from open knowledge. Moreover, the latest methods suffer from a significant "semantic gap" problem between the generated features of unseen classes and the real features of seen classes. To this end, this paper proposes a multi-view graph representation with a similarity diffusion model, applying the ZSL tasks to general data environments. This model applies a multi-view graph to enhance the semantics fully and proposes an innovative diffusion method to augment the graph representation. In addition, a feature diffusion method is proposed to augment the multi-view graph representation and bridge the semantic gap to realize zero-shot predicting. The results of numerous experiments in general data environments and on benchmark datasets show that the proposed method can achieve new state-of-the-art results in the field of general zero-shot learning. Furthermore, seven ablation studies analyze the effects of the settings and different modules of the proposed method on its performance in detail and prove the effectiveness of each module.

The study of microstructure of materials is of great significance in the field of materials science. The interdisciplinary cooperation of materials science and computer science makes it more accurate and efficient to explore the relationship between material microstructure and material properties. Machine learning has potential in exploring the relationship between microstructure and properties of materials. This paper proposes adding the morphology features of grains into the construction of grain knowledge graph to enrich the grain information in the graph. First, an autoencoder extracts the grain morphology features and adds them to the grain knowledge graph. Then, the graph convolutional network is used to extract the features of the graph, and the fully connected network is used to predict the properties of the material. Experiments are performed on actual EBSD scanning data. The experimental results show that the proposed method has noticeable improvement over the competing methods.

Zero-Shot Learning (ZSL) is an emerging research that aims to solve the classification problems with very few training data. The present works on ZSL mainly focus on the mapping of learning semantic space to visual space. It encounters many challenges that obstruct the progress of ZSL research. First, the representation of the semantic feature is inadequate to represent all features of the categories. Second, the domain shift problem still exists during the transfer from semantic space to visual space. In this paper, we introduce knowledge sharing (KS) to enrich the representation of semantic features. Based on KS, we apply a generative adversarial network to generate pseudo visual features from semantic features that are very close to the real visual features. Abundant experimental results from two benchmark datasets of ZSL show that the proposed approach has a consistent improvement.

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Net-work (MKFNet) alleviates the semantic insufficiency problem by fusing the domain information of different knowledge, which enables more relevant semantic features to be trained for semantic-to-visual feature embedding. The pro-posed knowledge regularization LKRgreatly improves the intersection between the synthesized visual features generated by MKFNet and the unseen visual features, which can alleviate the domain-shift problem. Empirically, we show that our approach consistently outperforms these state-of-the-art methods on a large number of available benchmarks on the generalized ZSL (GZSL).

Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. A key technology to seamlessly integrate different IoT devices into an IoT system is IoT middleware, a software system layer designed to be the intermediary between IoT devices and applications. However, two issues,communication gapandheterogeneous access, prevent the existing IoT middleware from effective application in an IoT system with heterogeneous standards and interfaces. To address this problem, inspired by a graph-based knowledge system for eliminating heterogeneity in business systems, we, in this article, propose a knowledge graph-based multilayer IoT middleware. The proposed multilayer IoT middleware introduces a new layer to bridge the gap between IoT devices with different communication protocols. It is able to uniformly manage all IoT devices by using an IoT knowledge graph. We evaluate the applicability of the proposed approach by a real-life IoT project, a remote monitoring project of rural sewage treatment stations located in Yunnan Province, China. We find that the proposed approach effectively resolves the communication gap and heterogeneous access problems that occurred in the system.

Since the ImageNet competition was held in 2012, the machine learning algorithm has performed very well on the image classification task. In object classification task, there are still some problems. For example, similar categories are difficult to be distinguished in images. In addition, with the increasing number of object categories, the background of scene has become another crucial issue in object classification. This paper focuses on image object recognition and makes two major contributions to tackle these issues. Firstly, we propose a semantic refinement method that analyzes the relationship among similar categories in images from the perspective of semantic knowledge. The knowledge of the relationship between semantics comes from a wide range of open knowledge. Secondly, we utilize the knowledge graph method to create the image knowledge graph with multiple categories in images. Our method exploits the knowledge from the adjacency matrix computed on train data to merge relevant classes into graph. We conduct extensive experiments on large-scale image datasets (ImageNet), demonstrating the effectiveness of our approach. Further, our method participates in ILSVRC 2012 challenges, and obtain the new state-of-the-art results on the ImageNet (82.43%).

Instance matching frameworks that identify links between instances, expressed as owl: sameAs assertions, have achieved a high performance while the performance of property matching lags behind. In this paper we leverage owl: sameAs links and show how these links can help for property matching. First, we extract all owl: sameAs instance pairs together with their properties and transform them into tables. Then, we apply table matching techniques and propose matching criteria to find relationships between properties. The experiments with real world LOD datasets show the efficiency and effectiveness of the proposed approach to deal with property matching.

Driven by the automation technologies and health informatics of Industry 4.0, hospitals in China have deployed a complete automation system/platform for healthcare services accessing. Without much more Internet knowledge, elderlies usually seek the third-party to assist them to get healthcare services from Web or APPs, it consequently results in an unexpected situation that scalpers could grab all healthcare services booking by unrighteous means in order to resell to elderlies for a much higher price. Moreover, it is hard for physicians to identify the scalpers due to the complexity, ad-hoc, and multiscenario nature of healthcare processes. In this paper, a novel method is proposed for the identification and creation of user groups of scalpers in mobile healthcare services. The approach utilizes and extends state of the art data analysis approaches in the event-logs of the mobile system to identify user groups. Based on the user groups, user profiles are extracted by identifying representative eventcases from hierarchical user-event clusters. A comprehensive evaluation is conducted in a selected test-set from the event-logs of a mobile healthcare APP. The result shows its accuracy and effectiveness in scalper detection in mobile healthcare APP. Further, a complete case study is deployed in a real word hospital to ensure its utility, efficacy, and reliability.

Information resource services are the key element for resource sharing in cloud manufacturing. Traditional resource service models focus on modeling the attributes, interfaces, and descriptions of the resources into resource information services. Such resource services are suitable for local environment but suffer semantic heterogeneities in open cloud environment. Recently, well-designed ontologies are applied in resource service models to unify the schema and eliminate the semantic heterogeneities among the services. However, the effectiveness of ontology-based models mainly depends on the expertise of the ontology experts in ontology designing. Moreover, it is difficult to catch the dynamic changes in the cloud once the ontology has been embedded. In this paper, a semantic model is presented for information resource service modeling that uses semantic links instead of ontologies. The model takes advantage of semantic links to enable automated integrating and distributed updating in resource service cloud. In the experiment, the model is applied on practical manufacturing resources from a wheel manufacturing company. The case study and experimental results show that the proposed model is suitable for modeling manufacturing resources into cloud services and enables the flexible and distributed manipulation on resource services in the cloud environment.

Ontology matching is an important part of enabling the semantic web to reach its full potential. Most existing ontology matching methods are mainly based on linguistic information (label, name, title and comment) but from the results achieved it is realized that this information is not sufficient. The latest ontology matching research works are trying to deeply dig into the structural information of ontologies by using "similarityflooding" method. However, there are several innate issues in similarity-flooding methods that lead to wrong matching results. In this paper, we report the problems of similarity-flooding in ontology matching and propose a novel method to effectively leverage the structural information of the ontology. The evaluation is conducted on OAEI ontology matching benchmarks from 2011 to 2015. The result shows that the proposed approach performs comparatively well with other state of the art matching systems.

Emergency clinical decision making is a challenging issue in healthcare services, notably in the environment of complicated data processing. Effective and efficient clinical decision making highly depends on the sufficient information sharing of the involved working teams. However, emergency decision support systems are usually hard to be developed because that the problems of emergency decision are always unexpected and unstructured. This paper focuses on the developing of decision support system to coordinate actions carried out in emergency situations. A framework is proposed based on MDA (Model-Driven Architecture) approach and resource model to dynamically build decision support system when emergency events occur. The effectiveness of our method is discussed and verified in a case study of collaborative clinical decision making on traffic accident emergency rescuing. The result shows that the MDA approach combined with resource model has the potential to support information system evolution along with the emergency events.

Internet of Things (IoT) software is required not only to dispose of huge volumes of real-time and heterogeneous data, but also to support different complex applications for business purposes. Using an ontology approach, a Configurable Information Service Platform is proposed for the development of IoT-based application. Based on an abstract information model, information encapsulating, composing, discomposing, transferring, tracing, and interacting in Product Lifecycle Management could be carried out. Combining ontology and representational state transfer (REST)-ful service, the platform provides an information support base both for data integration and intelligent interaction. A case study is given to verify the platform. It is shown that the platform provides a promising way to realize IoT application in semantic level.

The rapid development of Internet of things (IoT) technology makes it possible for connecting various smart objects together through the Internet and providing more data interoperability methods for application purpose. Recent research shows more potential applications of IoT in information intensive industrial sectors such as healthcare services. However, the diversity of the objects in IoT causes the heterogeneity problem of the data format in IoT platform. Meanwhile, the use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. In this research, first a semantic data model is proposed to store and interpret IoT data. Then a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, we present an IoT-based system for emergency medical services to demonstrate how to collect, integrate, and interoperate IoT data flexibly in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in a distributed heterogeneous data environment for supporting data accessing timely and ubiquitously in a cloud and mobile computing platform.