Metal component manufacturing requires stringent surface quality standards to prevent structural failures in critical applications. Metal surface defect detection remains challenging in resource-constrained industrial edge environments. Highly textured, non-stationary backgrounds easily obscure tiny defects with weak visual saliency. Furthermore, such defects exhibit pronounced anisotropic geometry. Existing models struggle to achieve global semantic understanding, accurate geometric alignment, and real-time inference under limited computational budgets. These limitations lead to frequent detection failures. EDGL-Net is proposed as an adaptive multi-scale detection architecture for edge deployment. EDGL-Net integrates global context modeling and anisotropic geometric feature extraction. It incorporates a parameter-sharing multi-scale prediction mechanism to enhance robustness for small and elongated defects. Experiments on the NEU-DET and GC10-DET datasets show that the proposed method achieves a favorable balance between detection accuracy and computational efficiency. EDGL-Net improves [email protected] by 2.7 points and Precision by 6.0 points over the baseline on NEU-DET. It consumes 64% of the computational resources required by mainstream models.

Multi-vehicle collaborative perception is fundamental to realizing Level 4+ autonomous driving by enabling connected vehicles to share and integrate sensor data for enhanced situational awareness. Under emerging Internet of Things (IoT) architectures, fleets of vehicles form dynamic, decentralized networks. In practice, however, deployment is hampered by three core challenges: (1) transmission latency in vehicle-to-vehicle (V2V) links, (2) data transmission constrained by limited communication bandwidth, and (3) the complexity of fusing asynchronous, multi-source data streams. To overcome these obstacles, this paper presents STCo, a spatio-temporal context-aware and communication-efficient framework for multi-vehicle collaborative perception. First, a spatio-temporal context modeling mechanism is devised to enhance perceptual continuity and mitigate communication asynchrony in IoT environments. Second, a cross-vehicle sensor perspective disparity mining algorithm leverages distributed observations to extract high-value complementary information. Third, a multi-source data fusion paradigm unifies diverse perception inputs into a unified representation from the ego vehicle’s perspective, thereby strengthening feature correlations. This paper validates STCo on both the real-world V2V4Real dataset and the large-scale simulated OPV2V benchmark. Experimental results demonstrate that STCo outperforms state-of-the-art methods in detection accuracy while substantially reducing communication overhead, highlighting its efficacy and practicality for IoT-enabled autonomous driving systems.

Large Language Models (LLMs) offer groundbreaking advancements in recommender systems through superior text analysis and decision-making support. However, integrating LLMs into recommender systems still suffers from the problems of identifier uninterpretability and lack of transparency. To address these issues and fully leverage the capabilities of LLMs, we propose a chain of thought (CoT) based recommendation framework called CoT4Rec which employs LLMs as data enhancers for user preference analysis. Initially, we design a CoT reasoning strategy that can derive more behaviorally-aligned user preference features by clustering users’ historical interactions. Subsequently, we propose a two-stage recommendation model that not only makes full use of the world knowledge embedded in LLMs but also generates a logically transparent reasoning path. By integrating a user preference analyzer early in the recommendation pipeline, the model deeply analyzes users' historical interactions, helping to enhance the personalization and transparency of the recommender system. CoT4Rec demonstrates superior performance over existing state-of-the-art models in recommendation tasks across four public datasets, achieving improvements ranging from 2.2% to 12.2%.

To maintain the company's talent pool, recruiters need to continuously search for resumes from third-party websites (e.g., LinkedIn, Indeed). However, fetched resumes are often incomplete and inaccurate. To improve the quality of third-party resumes and enrich the company's talent pool, it is essential to conduct duplication detection between the fetched resumes and those already in the company's talent pool. Such duplication detection is challenging due to the semantic complexity, structural heterogeneity, and information incompleteness of resume texts. To this end, we propose MHSNet, an multi-level identity verification framework that fine-tunes BGE-M3 using contrastive learning. With the fine-tuned BGE-M3, MHSNet generates multi-level sparse and dense representations for resumes, enabling the computation of corresponding multi-level semantic similarities. Moreover, the state-aware Mixture-of-Experts (MoE) is employed in MHSNet to handle diverse incomplete resumes. Experimental results verify the effectiveness of MHSNet.

In the field of Web knowledge base mining and Web services, the recognition service for named entity faces many challenges such as context complexity, semantic subtlety, and fuzzy entity boundaries, all of which require highly accurate and robust recognition service. The current services usually fail to reach those conditions. To address this issue, this paper proposes a recognition service for named entities for large Web knowledge bases, and the core contrition is the developed dual multilayer feature learning (D-MLFL) service, which combines projected gradient descent (PGD), adversarial learning, and a fused attention mechanism. Our service successfully addresses the challenges of complex context and subtle semantics faced by named entity recognition tasks. Our service integrates deep language models, recurrent neural networks, and conditional random fields, and clearly outperforms existing approaches in many sub-tasks, including in feature extraction, sequence modeling, and label decoding, especially in dealing with complex and diverse entity types and contextual relationships. We performed sufficient experiments, and the results show that our service significantly enhances the robustness against noise and abnormal Web data. The ability to extract entity features is improved, resulting in higher accuracy in identifying entities with fuzzy boundaries and complex semantics.

In the field of multivariate time series analysis, data incompleteness, such as missing data due to measurement errors or equipment failures, is a common issue that severely hinders in-depth analysis and decision-making for downstream tasks. Existing time series imputation models have made progress in fitting the overall data distribution but lack the ability to analyze data features from multiple perspectives. This limitation hinders a deeper understanding of the data features, thereby affecting the reliability and generalization performance of the imputation results. For the limitation of existing models, this paper proposes a novel time series imputation model based on multiple guidance distillation, MGDTSI. This model uses self-distillation, by using teacher models of different majors, to strengthen the ability of student model. To distinguish the guiding capabilities of different teachers, the weight fusion module is used to assign weights to different teacher models to jointly guide the student model. The experimental results conducted on multiple public time series datasets show that MGDTSI significantly outperforms the existing baseline methods in the task of missing value imputation.

The rapid evolution of the online fashion industry has intensified the demand for interactive fashion retrieval systems capable of precise and flexible searches based on user-specified attribute modifications. However, prevailing fashion retrieval methods often overlook the distinctive distributional properties of fashion images and struggle to preserve semantic consistency during attribute manipulation. To address these limitations, we propose DiSCo, a novel disentangled attribute manipulation retrieval framework via semantic reconstruction and consistency regularization. Our approach comprises three key components: (1) An attribute-aware manipulation network that constructs target fashion embeddings through cross-modal attribute modification deltas, leveraging dedicated fashion attribute encoders; (2) A cross-modal semantic reconstruction network that synthesizes target images directly from modified attribute descriptions, supervised by adversarial and attribute classification losses to ensure interpretable edits; (3) An adaptive fusion mechanism that dynamically integrates attribute-modified embeddings with reconstructed image features. Extensive evaluations on two benchmark datasets (DeepFashion and Shopping100K) demonstrate that DiSCo achieves superior retrieval accuracy over state-of-the-arts while maintaining high-fidelity editing. Quantitative and qualitative analyses further confirm that DiSCo generates more realistic fashion representations, underscoring its effectiveness in attribute-aware retrieval tasks.

In the resource-constrained Internet of Things (IoT)-edge computing environment, split federated (SplitFed) learning is implemented to enhance training efficiency. This method involves each terminal device dividing its full deep neural network (DNN) model at a designated layer into a device-side model and a server-side model, then offloading the latter to the edge server. However, existing research overlooks four critical issues as follows: 1) the heterogeneity of end devices’ resource capacities and the sizes of their local data samples impact training efficiency; 2) the influence of the edge server’s computation and network resource allocation on training efficiency; 3) the data leakage risk associated with the offloaded server-side submodel; and 4) the privacy drawbacks of current centralized algorithms. Consequently, proactively identifying the optimal cut layer and server resource requirements for each end device to minimize training latency while adhering to data leakage risk rate constraint remains a challenging issue. To address these problems, this article first formulates the latency and data leakage risk of training DNN models using SplitFed learning. Next, we frame the SplitFed learning problem as a mixed-integer nonlinear programming challenge. To tackle this, we propose a decentralized proactive model offloading and resource allocation (DP-MORA) scheme, empowering each end device to determine its cut layer and resource requirements based on its local multidimensional training configuration, without knowledge of other devices’ configurations. Extensive experiments on two real-world datasets demonstrate that the DP-MORA scheme effectively reduces DNN model training latency, enhances training efficiency, and complies with data leakage risk constraints compared to several baseline algorithms across various experimental settings.

The advent of single-cell RNA-sequencing (scRNA-seq) technology promotes biological analysis at the cellular level. Clustering cells to identify the type of cell is an important step in scRNA-seq analysis. Most of the existing clustering methods based on deep learning technology first adopt an autoencoder-decoder module to learn the low-dimensional features of cells and then apply other modules to learn the clustering relationship features of cells. However, the two-stage learning process makes the model training more difficult. Here we propose a novel cell representation learning method that is based on a local self-attention network and contrastive learning for scRNA-seq clustering. In particular, a local self-attention network automatically aggregates potential information of cells based on a cell relationship graph, and a dual contrastive learning module simultaneously optimizes the cell representation in cell- and cluster-level. The cell-level module makes related cells similar at the feature level, whereas the cluster-level module enables cells to form clusters at the cluster level. Finally, the powerful Leiden community discovery algorithm is used for clustering based on learned representation. In brief, we construct cell pairs through cell relationships and utilize contrastive learning to directly learn cell representations in a low-dimensional space while preserving their local structural relationships without pretraining an autoencoder-decoder module. Three benchmark experiments on 160 subsample datasets with different numbers of cell types, 3 datasets of different protocols, and 9 real public datasets demonstrate the superior performance of the proposed method compared with baseline methods.

In computational social systems, recommendation functionality plays a pivotal role in influencing user behavior, enhancing user experience, and driving engagement. To help recommendation functionality to better suggest relevant content or items, the social platforms usually utilize large-scale knowledge discovery techniques to analyze trends in user interactions and extract patterns from large datasets. Click-through rate (CTR) prediction is crucial in recommendation systems for measuring effectiveness, understanding user behavior, training and optimizing models, impacting business outcomes, enhancing personalization, and identifying issues. It provides actionable insights that assist in continuously refining and improving the recommendation process. Traditional deep learning-based CTR prediction models cannot work well for recommendation in social systems due to the data sparsity and the long-tail data problems since the representation learned from the user behavior is basically dominated by the major part of the data. In this article, we propose a multitask-based self-supervised learning model (MTSSL) that can better deal with sparse and long-tail user interaction data. Specifically, we first transform the CTR prediction task into the multitask joint learning framework with a set of shared subnetworks. Each subnetwork learns a representation of the entire user data, and hence, the sparse and long-tail data would have opportunity to fall into the best matched representation space of historical user behavior. Moreover, two kinds of self-supervision signals are employed to guide the learning of the representations. Extensive experiments over four user interaction datasets demonstrate the superiority of our proposed MTSSL over state-of-art models for recommendations. In terms of online A/B test, our model achieves around 3% better performance than the counterparts.

In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city.

Action quality assessment (AQA) has become crucial in video analysis, finding wide applications in various domains, such as healthcare and sports. A significant challenge faced by AQA is the background bias due to the dominance of the background in videos. Especially, the background bias tends to overshadow subtle foreground differences, which is crucial for precise action evaluation. To address the background bias issue, we propose a novel data augmentation method named Scaled Background Swap. First, the background regions between different video samples are swapped to guide models focus toward the dynamic foreground regions and mitigate its sensitivity to the background during training. Second, the video’s foreground region is upscaled to further enhance models’ attention to the critical foreground action information for AQA tasks. In particular, the proposed Scaled Background Swap method can effectively improve models’ accuracy and generalization by prioritizing foreground motion and swapping backgrounds. It can be flexibly applied with various video analysis models. Extensive experiments on AQA benchmarks demonstrate that Scaled Background Swap method achieves better performance than baselines. Specifically, the Spearman’s rank correlation on datasets AQA-7 and MTL-AQA reaches 0.8870 and 0.9526, respectively. The code is available at: https://github.com/Emy-cv/Scaled-Background Swap.

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.

Abstract Software maintenance and evolution play an important role in the software engineering field, especially when current software becomes more and more complex and powerful. As an entity to implement business processes and gain revenue, valuable software is composed of business logic and corresponding organization role interaction interfaces. With the enterprise development, the organization architecture also evolves, like expanding, cross department cooperation, and so on. However, existing software process adaptive approaches mainly focus on handling the change of the business (program) logic instead of organization structure. Therefore, we propose an adaptive software business process approach that supports organization architecture evolution and automatically migrates the run‐time process instances to the latest version. First, a business process adaptation model is designed, which includes the organization layer, business process layer and event layer that connects the two. Based on the model, the organization changing impact and business process model modification are formalized. Besides, the business process adaptation approach is designed. According to the dependence between the organization architecture and the business process activities, the affected domain detection algorithms for three basic business process structures and the business process instance migration algorithm are developed. Finally, the feasibility and stability of the proposed system are comprehensively evaluated with the synthetic data sets.

Fully supervised semantic segmentation has performed well in many computer vision tasks. However, it is time-consuming because training a model requires a large number of pixel-level annotated samples. Few-shot segmentation has recently become a popular approach to addressing this problem, as it requires only a handful of annotated samples to generalize to new categories. However, the full utilization of limited samples remains an open problem. Thus, in this article, a mutually supervised few-shot segmentation network is proposed. First, the feature maps from intermediate convolution layers are fused to enrich the capacity of feature representation. Second, the support image and query image are combined into a bipartite graph, and the graph attention network is adopted to avoid losing spatial information and increase the number of pixels in the support image to guide the query image segmentation. Third, the attention map of the query image is used as prior information to enhance the support image segmentation, which forms a mutually supervised regime. Finally, the attention maps of the intermediate layers are fused and sent into the graph reasoning layer to infer the pixel categories. Experiments are conducted on the PASCAL VOC-$5^i$dataset and FSS-1000 dataset, and the results demonstrate the effectiveness and superior performance of our method compared with other baseline methods.

In edge environment, many smart application instances are triggered randomly by resource-constrained Internet of Things (IoT) devices. These application instances usually consist of dependent computation components, which can be modeled as workflows in different shapes and sizes. Due to the limited computing power of IoT devices, a common approach is to schedule partial computation components of multiple workflow instances to the resource-rich edge servers to execute. However, how to schedule the stochastically arrived multiple workflow instances in edge environment with the minimum average completion time is still a challenging issue. To address such an issue, in this paper, we adopt the graph convolution neural network to transform multiple workflow instances with different shapes and sizes into embeddings, and formulate the online multiple workflow scheduling problem as a finite Markov decision process. Furthermore, we propose a policy gradient learning-based online multiple workflow scheduling scheme (PG-OMWS) to optimize the average completion time of all workflow instances. Extensive experiments are conducted on the synthetic workflows with various shapes and sizes. The experimental results demonstrate that the PG-OMWS scheme can effectively schedule the stochastically arrived multiple workflow instances, and achieve the lowest average completion time compared with four baseline algorithms in edge environments with different scales.

Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effectively deal with heterogeneity in it. In this paper, we propose a M ulti-view E nhanced G raph A ttention N etwork (named MEGAN ) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users’ listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users’ hybrid preferences are learned from users’ listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods.

Most of the existing personalized recommendation methods predict the probability that one user might interact with the next item by matching their representations in the latent space. However, as a cognitive task, it is essential for an impressive recommender system to acquire the cognitive capacity rather than to decide the users' next steps by learning the pattern from the historical interactions through matching-based objectives. Therefore, in this paper, we propose to model the recommendation as a logical reasoning task which is more in line with an intelligent recommender system. Different from the prior works, we embed each query as a box rather than a single point in the vector space, which is able to model sets of users or items enclosed and logical operators (e.g., intersection) over boxes in a more natural manner. Although modeling the logical query with box embedding significantly improves the previous work of reasoning-based recommendation, there still exist two intractable issues including aggregation of box embeddings and training stalemate in critical point of boxes. To tackle these two limitations, we propose a Contrastive Box learning framework for Collaborative Reasoning (CBox4CR). Specifically, CBox4CR combines a smoothed box volume-based contrastive learning objective with the logical reasoning objective to learn the distinctive box representations for the user's preference and the logical query based on the historical interaction sequence. Extensive experiments conducted on four publicly available datasets demonstrate the superiority of our CBox4CR over the state-of-the-art models in recommendation task.

During the development of software systems, even with advanced planning, problems with quality and security occur. These defects may result in threats to program development and maintenance. Therefore, to control and minimise these defects, machine learning can be used to improve the quality and security of software systems. This special issue focuses on recent advances in architecture, algorithms, optimisation, and models for machine learning applied to quality and security in software systems. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 4 manuscripts. A summary of these accepted papers is outlined below. In the first paper entitled “Robust Malware Identification via Deep Temporal Convolutional Network with Symmetric Cross Entropy Learning” by Sun et al., the authors propose a robust Malware identification method using the temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface (API) function names in many cases. Here, considering the numerous unlabelled samples in practical intelligent environments, the authors pre-train the TCN model on an unlabelled set using a word embedding method, that is, word2vec. In the experiments, the proposed method is compared to several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real-world dataset. The performance comparisons demonstrate the better performance and noise robustness of the proposed method, that the proposed method can yield the best identification accuracy of 98.75% in real-world scenarios. In the second paper entitled “Just-In-Time Defect Prediction Enhanced by the Joint Method of Line Label Fusion and File Filtering” by Zhang et al., the authors propose a Just-in-Time defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF). First, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Second, to obtain semantics-enhanced code representation, the authors propose a cross-attention-based line label fusion method to perform complementary feature enhancement. Third, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF predicts defective software changes more effectively. In the third paper entitled “Android Malware Detection via Efficient API Call Sequences Extraction and Machine Learning Classifiers” by Wang et al., the authors propose a novel Android malware detection framework, where the authors contribute an efficient API call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, the authors propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids unnecessary repetitive path searching. The authors also propose a pruning search, which further reduces the number of paths to be searched. The developed algorithm greatly reduces the time complexity. The authors generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real-world APKs, and the results demonstrate that the proposed method reduces the running time and produces high detection accuracy. In the fourth paper entitled “Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction” by Zheng et al., the authors propose H-BRP, a Hybrid Blockchain Reliability Prediction model, to extract the blockchain reliability factors and then make the personalised prediction for each user. Connecting to unreliable blockchain peers is prone to resource waste and even loss of cryptocurrencies by repeated transactions. The proposed model primarily aims to select reliable blockchain peers and to evaluate and predict their reliability. Comprehensive experiments conducted on 100 blockchain requesters and 200 blockchain peers demonstrate the effectiveness of the proposed H-BRP model. Furthermore, the implementation and dataset of 2,000,000 test cases are released. The Guest Editors would like to express their deep gratitude to all the authors who have submitted their valuable contributions, and to the numerous and highly qualified anonymous reviewers. We think that the selected contributions, which represent the current state of the art in the field, will be of great interest to the community. We also would like to thank the IET Software publication staff members for their continuous support and dedication. We particularly appreciate the relentless support and encouragement granted to us by Prof. Hana Chockler, the Editor-in-Chief of IET Software. Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a Professor at the College of Future Industry, Gachon University, Korea. His research interests include Software Intelligence, Cloud/Edge Computing, and AI4Healthcare. He has publications in IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TMM, IEEE TSC, IEEE TCC, IEEE TFS, IEEE TNSE, IEEE TNSM, IEEE TCCN, IEEE TGCN, IEEE TCSS, IEEE TETCI, IEEE TCE, IEEE/ACM TCBB etc. He has broad working experience in cooperative industry-university-research. He is a European Union Institutions-appointed external expert for reviewing and monitoring EU Project, is a member of the EPSRC Peer Review Associate College for UK Research and Innovation in the UK, and a founding member of the IEEE Computer Society Smart Manufacturing Standards Committee. Prof. Gao is a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the British Computer Society (BCS), and a Member of the European Academy of Sciences and Arts (EASA). Dr. Walayat Hussain is a Visiting Fellow at the School of Computer Science. Currently he is a Senior Lecturer and the Head of Discipline-IT at the Australian Catholic University, Australia. He served as a Lecturer and Postdoctoral Research Fellow at the Victoria University, Melbourne, School of Information, Systems and Modelling, University of Technology Sydney Australia for several years. Prior to joining UTS, he worked as an Assistant Professor and the Postgraduate program coordinator at BUITEMS University for many years. Walayat's research areas are Distributed Systems, AI, Information Systems, Computational Intelligence, Machine Learning, Business Intelligence, Decision Support Systems, and Usability Engineering. His work has been published in different top-ranked reputable ERA-A*, A, Q1 journals and conferences such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Service Computing, Future Generation Computer Systems, Information Sciences, International Journal of Intelligent Systems, Information Systems, Journal of Ambient Intelligence and Humanized Computing, Neural Computing and Applications, The Computer Journal (Oxford University Press), Computer & Industrial Engineering, IEEE Access, ACM TOMM, IEEE TGCN, IEEE TETCI, International Journal of Communication Systems, Mobile Networks and Applications, GJFSM, FUZZ-IEEE, ICONIP, and many others. Ramón J. Durán Barroso received the degree in telecommunication engineering and the Ph.D. degree from the University of Valladolid, Spain, in 2002 and 2008 respectively. He currently works as an Associate Professor with the University of Valladolid. He is also the Coordinator of the Spanish Research Thematic Network “Go2Edge: Engineering Future Secure Edge Computing Networks, Systems and Services” composed of 15 entities and the H2020 IoTalentum Project. He has authored more than 150 papers in international journals and conferences. His current research interests include the use of artificial intelligence techniques for the design, optimisation, and operation of future heterogeneous networks, multi-access edge computing, and network function virtualisation. Dr. Junaid Arshad has 14 years of research experience and expertise in investigating and addressing cybersecurity challenges for diverse computing paradigms such as Grid computing, Cloud computing, IoT, and blockchain. He is actively engaged in cutting-edge R&D distributed ledger technologies including blockchains, Tangle and Hashgraphs, investigating novel challenges to improve state of the art for such technologies as well as their use to solve real-world challenges. Junaid is an alumnus of the Innovate UK & DCMS funded CyberASAP programme, commercially prototyping the CyMonD system for effective monitoring and defence of IoT-based systems against cyber-threats. Junaid has successfully achieved research funding from UK and overseas funding agencies, and has worked as a security specialist for a number of EU funded projects with experience of developing bespoke security solutions. He is also actively involved in research surrounding analysis of malware for mobile and IoT devices focusing on profiling malicious behavior to achieve runtime detection and defense. Junaid has successfully published high quality research within cybersecurity and has more than 50 publications at high quality venues including journals, book chapters, conferences and workshops. He is an Associate Editor for the Cluster Computing and IEEE Access journals and regularly serves on program and review committees of several journals and conferences. Yuyu Yin received the Ph.D. degree in computer science from Zhejiang University in 2010. He is currently a Professor with the College of Computer, Hangzhou Dianzi University, Hangzhou, China. He is also a Supervisor of master’s students with the School of Computer Engineering and Science, Shanghai University, Shanghai, China. He has authored or coauthored more than 40 articles in journals and refereed conferences, such as Sensors, Entropy, IJSEKE, Mobile Information Systems, ICWS, and SEKE. His research interests include service computing, cloud computing, and business process management. Dr. Yin is also a member of the China Computer Federation (CCF) and the CCF Service Computing Technical Committee. He has organised more than ten international conferences and workshops, such as FMSC 2011–2017 and DISA 2012 and 2017–2018. He has served as a Guest Editor for the Journal of Information Science and Engineering and International Journal of Software Engineering and Knowledge Engineering and a Reviewer for the IEEE Transaction on Industry Informatics, Journal of Database Management, and Future Generation Computer Systems.

Smart cities are designed to satisfy the needs of residents and improve their quality of life by providing a wide range of smart city services. One of the keys to the efficient operation of smart city services is the accurate forecast of the missing Quality of Service (QoS). Presently, many approaches utilize the context information of users and services, such as geographic location and network location, to somewhat increase the prediction accuracy and forecast the missing QoS values. However, because the network conditions and server status are unpredictable, time is also considered as one of the important factors affecting QoS prediction, which brings more challenges as follows: higher data dimension, more complex data characteristics, and higher data sparsity. To overcome these challenges, we propose an approach for time-aware Web service QoS prediction based on contrastive learning (named CLpred). CLpred utilizes a sequential data input format for QoS data and models these QoS sequences through transformer encoder with CLpred framework. Therefore, it can downscale QoS data and extract a more efficient representation in complex QoS data. Furthermore, it makes it possible to apply data augmentation methods to address the problems of data sparsity. In order to prove the superiority of the proposed approach, particularly inside the presence of extremely high-data sparsity, extensive experiments are conducted on the well-known service QoS data set WSDREAM.

Biomedical Named Entity Recognition (BioNER) aims at identifying biomedical entities such as genes, proteins, diseases, and chemical compounds in the given textual data. However, due to the issues of ethics, privacy, and high specialization of biomedical data, BioNER suffers from the more severe problem of lacking in quality labeled data than the general domain especially for the token-level. Facing the extremely limited labeled biomedical data, this work studies the problem of gazetteer-based BioNER, which aims at building a BioNER system from scratch. It needs to identify the entities in the given sentences when we have zero token-level annotations for training. Previous works usually use sequential labeling models to solve the NER or BioNER task and obtain weakly labeled data from gazetteers when we don't have full annotations. However, these labeled data are quite noisy since we need the labels for each token and the entity coverage of the gazetteers is limited. Here we propose to formulate the BioNER task as a Textual Entailment problem and solve the task via Textual Entailment with Dynamic Contrastive learning (TEDC). TEDC not only alleviates the noisy labeling issue, but also transfers the knowledge from pre-trained textual entailment models. Additionally, the dynamic contrastive learning framework contrasts the entities and non-entities in the same sentence and improves the model's discrimination ability. Experiments on two real-world biomedical datasets show that TEDC can achieve state-of-the-art performance for gazetteer-based BioNER.

With the development of the Industrial Internet of Things system, the huge amount of devices, services, and continuous data, making it difficult to discover a trusted service in complex scenarios. To better leverage knowledge and historical behavior, recommendation systems are applied. However, the model accuracy closely depends on training data size; there is a great risk of data leaking by collecting from multiple departments. To solve these problems, we propose a graph-convolutional-neural-network-based federated approach, which accurately recommends proper service for participating clients without gathering the raw data. Specifically, each client trains locally and uploads the weights of their model to the server for aggregation. Besides, the potential overlapping services of different clients are leveraged to guide the embedding aggregation and sharing, which, in turn, optimize the local training results. Their sensitive scenarios' embedding is kept locally. Owing to the model aggregation, it also resists the poisoning attack to some degree. In addition, the comprehensive experiments on classic public recommendation datasets evaluate the feasibility, effectiveness, trustworthiness, and potential influences.

AI-empowered 5G/6G networks play a substantial role in taking full advantage of the Internet of Things (IoT) to perform complex computing by offloading tasks to edge services deployed in intelligent transport systems. However, offloading behavior has a certain regularity, and the real-time location of users can easily be inferred by attackers who have historical user data during the data transmission process. To address this problem, a privacy-oriented task offloading method that can resist attacks from privacy attackers with prior knowledge is proposed. First, the local computing model, channel model, and privacy loss model are defined and used to quantify evaluation indicators, such those related to privacy, time, and energy. Among them, privacy loss is formalized as the probability of a successful attack by an attacker with prior knowledge. Second, the process of solving an optimal task offloading decision problem is formalized into a Markov decision process (MDP). Finally, the deep reinforcement learning (DRL) method PPO2 is proposed to solve the planning problem of task offloading with good generalization and convergence speed, where we focus on the location privacy requirement. Experiments show that our method can handle large-scale task offloading and obtain offloading policies with reduced privacy loss, energy consumption and time delays.

Although some existing models are proposed to exploit reviews for improving performance for recommender systems, few of them can handle the following issues led by the insufficient review data: (i) The regular training process does not exactly fit the scenario of preference prediction with few historical behaviors. (ii) Extracting informative and sufficient semantic features from limited review texts is a challenging work. To alleviate these issues, this paper proposes an enhanced prototypical network, FS-EPN, that leverages reviews for recommendation under the few-shot setting. FS-EPN consists of an attentional prototypical network being the basic architecture, a sentiment encoder and a memory collector cooperating to capture the extra sentimental and collaborative information from both user and item perspectives for semantic information supplement. We train FS-EPN under the meta-learning framework, which models the training process in the episodic manner to mimic the few-shot test environment. Extensive experiments conducted on six publicly available datasets demonstrate the superior capability of FS-EPN over several state-of-the-art models in few-shot recommendation.

Abstract Software testing is an indispensable part of the software engineering industry, which guarantees product reliability and safety. Traditional testing approaches face the testing Oracle problem, they are difficult to construct the expected outputs with the increasing of program complexity. As a result, metamorphic testing, which tests the program by examining the relationship between the execution results, is proposed. However, existing manual metamorphic relation construction requires huge effects of domain experts, and automatic methods are unstable and inefficient due to the insufficient software feature mining. Hence, we proposed a multi‐dimensional program structure‐based metamorphic relation prediction approach, which is composed of feature extraction and prediction model building. In the feature extraction stage, the testing program is converted to multiple intermediate structures (such as control flow graphs and abstract syntax trees) to explore its features. In the prediction model building stage, the extracted feature set is used as the training set, and a novel semi‐supervised support vector machine‐bagging‐K‐nearest neighbors algorithm is designed to train the prediction model. Besides, a two‐phase hybrid granularity search algorithm is proposed to improve the prediction performance by selecting the optimal number of weak classifiers. Compared with existing approaches, our proposed model can improve the accuracy by around 14%.

Many mobile apps of social Internet of Things (sIOT) systems can help us record and share daily events, such as health and sport events. In fact, healthy diet recognition is an important and challenging problem in dish health assessment. Via the collection and monitoring of data pertaining to our daily diet, we can work in collaborative ways to achieve dish image annotation based on sIOT systems to enhance deep features. To this end, this article proposes a deep feature and attention mechanism-based method for dish health assessment, which aims to apply a hand-deep local–global net (HDLGN) for dish image recognition. Then, food taste is used as health guidance for people who want to lose weight or follow doctors’ advice. First, the local attention mechanism is introduced to identify key areas of the dish image. Second, ingredient and handcrafted color features are extracted to learn deep features. Subsequently, we combine local and global attention mechanisms to return the dish taste as the recognition result. Finally, experiments show that our proposed method can effectively improve the accuracy of taste recognition.

Online travel platforms (OTPs), for example, booking.com, Ctrip.com, and Fliggy, deliver travel experiences to online users by providing travel-related products. Hotel recommendation is significantly important for OTPs since hotel bookings account for almost half of the travel expenses and hotel products generate more than half of OTP’s revenues. More than 58% Fliggy users may choose to use query-less hotel searches to find candidate hotels, where no additional keywords are given except the expected check-in date and travel destination city. Thus, how to recommend hotels to traveler users is important and challenging. In this article, we explore the unique characteristics of query-less hotel users and propose a novel multiscenario query-less search network (MSQS). According to their searching date, expected check-in date, current city, and expected hotel city, MSQS groups users’ behaviors (e.g., click, purchase, search) into four scenario groups, namely today-local, today-nonlocal, future-local, and future-nonlocal. The key components of MSQS are the global expert, the scenario expert, and the feedback expert. The global expert learns common features among different scenarios and extracts the feature interactions between context, users, and hotels. The scenario expert utilizes multilayer perception to learn the differentiating features between scenarios. The feedback expert learns users’ preferences for hotels in different scenarios through their historical behaviors, and a scenario interest extractor is carefully designed to enhance attention across scenarios and behaviors. An offline experiment on the Fliggy production dataset with over 8 million users and 0.49 million travel items and an online A/B test both show that MSQS effectively predicts users’ hotel booking intentions.

Vehicular ad hoc networks (VANETs) provide self-organized wireless multihop transmission, where nodes cooperate with each other to support data communication. However, malicious nodes may intercept or discard data packets, which might interfere with the transmission process and cause privacy leakage. We consider historical interaction data of nodes as an important factor of trust. Thus, this paper focuses on the trust node management of VANETs, which aims to quantify node credibility as an assessment method and avoid assigning malicious nodes. First, the integrated trust of each node is proposed, which consists of the direct trust and the recommended trust. The former is dynamically computed by historical interaction records and Bayesian inference considering penalty factors. The latter defines trust by third-party nodes and their reputation. Second, the process of trust calculation and data communication calls for timeliness. Therefore, we introduce a time sliding window and time decay function to ensure that the latest interaction information has a higher weight. We can sensitively identify malicious nodes and make quick responses. Finally, the experimental results demonstrate that our proposed method outperforms bassline methods, especially with respect to the packet delivery ratio and security.

Nowadays, artificial neural networks are widely used for users’ online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users’ intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users’ intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users’ intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users’ actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy.

Traffic context plays an important role in supporting automated driving and intelligent transportation systems. Smart vehicles explore surrounding environments by analyzing sensor data and periodically communicating with neighbors and road infrastructures. The context can be well learned in this way to support driving, but the vehicle trajectory can be also easily exposed under eavesdropping attacks. The pseudonym is proposed to hide the real identity of the vehicles. However, the effectiveness of anonymity, the safety of driving, the convenience of implementation and the utilization of resources in previous approaches have not been well-balanced. Therefore, focusing on efficiently replacing pseudonyms with the premise of ensuring driving safety, we propose a secure dynamic silent mix zone pseudonym changing scheme (TLAS) based on the real-time traffic context prediction for urban regions. It naturally takes the area in front of the red traffic light as a silent mix zone, which avoids the driving security issue caused by signal silence. Besides, the area length is dynamically configured according to the traffic context predicted in the last green light cycle, so the anonymous effect can be improved. In addition, considering the resource utilization and accuracy requirement, the adaptive prediction algorithm is applied. We conduct simulation experiments with real-world traffic history using SUMO and OMNET++, the results show that TLAS strategy can indeed achieve a better anonymous effect (reducing standardized traceability rate by 8.2%) with lower driving speed for safety concern.

As the emerging paradigm that is believed to be conducive to the development of intelligent transportation systems (ITS), Internet of Vehicles (IoV) is constructed with a number of connected heterogeneous vehicle devices which provide a variety of services. As the number of vehicle devices in IoV is growing fast, selecting the appropriate service from candidate services which are functionally equivalent is becoming an imperative task. Predicting the non-functional attribute of service invocation, namely quality of service (QoS), to ensure the optimal service selection is the mainstream direction. Considering that most of the conventional prediction methods neglect the fact that QoS values change dynamically with some objective factors, this paper proposes a recurrent neural network based collaborative filtering method called RNCF for QoS prediction. Specifically, a multi-layer GRU structure is incorporated in the framework of neural collaborative filtering to model the dynamic state of physical environments or network conditions and share the invocation records across different time slices. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the proposed QoS prediction model RNCF.

Human motion understanding and prediction is an integral aspect in our pursuit of machine intelligence and human-machine interaction systems. Current methods typically pursue a kinematics modeling approach, relying heavily upon prior anatomical knowledge and constraints. However, such an approach is hard to generalize to different skeletal model representations, and also tends to be inadequate in accounting for the dynamic range and complexity of motion, thus hindering predictive accuracy. In this work, we propose a novel approach in modeling the motion prediction problem based on stochastic differential equations and path integrals. The motion profile of each skeletal joint is formulated as a basic stochastic variable and modeled with the Langevin equation. We develop a strategy of employing GANs to simulate path integrals that amounts to optimizing over possible future paths. We conduct experiments in two large benchmark datasets, Human 3.6M and CMU MoCap. It is highlighted that our approach achieves a 12.48% accuracy improvement over current state-of-the-art methods in average.

With the prevalence of web techniques and Internet-of-Things networks, an increasing number of developers build software by invoking existing application programming interfaces (APIs), especially in industrial systems. As the number of existing APIs in industrial systems is large, it is critical to recommend suitable APIs from big APIs data to developers in industrial software development. There have been some approaches proposed for APIs recommendation, but the existing approaches focus on the utilization of historical invocation records but ignore the exploitation of other information in the development process. We find that this ignored information can be mined as cognitive knowledge to learn the behavior rules of developers. In this article, we propose a holistic personalized recommendation framework that contains two individual models and one ensemble model, which are based on joint matrix factorization and cognitive knowledge mining. In the two individual models, we study the hidden relationships among users, which are mined from the APIs following records. We also study the hidden relationships among APIs, which are mined from the content information. We also propose an ensemble model. We crawled a large real-word dataset and conducted sufficient experiments, and compared our framework with well-known existing methods. The experimental results demonstrate that our framework achieves the best performance.

The explosive growth of the Internet of Things and modern networking technologies lay the foundation for the development of intelligent transportation systems and smart cities. To analyzing massive data under the required time for transportation issues, the edge computing paradigm is applied, which pre-processing large amounts of data at the network edge to save bandwidth and improve response time. However, data reliability and security are still facing many challenges in the edge environment. In this article, we propose a multi-authority hybrid attribute-based signature scheme (SDABS). It is composed of four phases: system initialization, signature generation, signature verification, and attribute revocation phases. To better describe frequently changing features in the transportation systems like location, the dynamic attribute is introduced in building the signature. The multi-layer policy tree is applied to support flexible and various access policies, which also naturally form user groups and help data searching. Besides, the multi-authority structure is more suitable for the distributed edge environment. We evaluate SDABS from both theoretical analysis and practical analysis. Compared with two classical signature schemes (MABS and ODMA-ABS), experimental results demonstrate that the proposed SDABS can achieve better performance at an acceptable cost in the terms of attributes and attribute authorities.

With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact that most existing QoS prediction models are unable to effectively address the data-sparsity problem, a novel two-stage framework called AgQ is proposed for QoS prediction. Specifically, a data augmentation strategy is designed in the first stage to enlarge the training set by drawing additional virtual instances. In the second stage, a prediction model is applied that considers both virtual and factual instances during the training procedure. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the our QoS prediction framework and verify that the data augmentation strategy can indeed alleviate the data-sparsity problem. In terms of mean absolute error, taking the Multilayer Perceptron model as an example, the maximum improvement achieves 5% under 5% sparsity.

This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task. We propose to mimic the high-level user preference other than the raw interaction history based on the side information for the fresh users. Specifically, an attentive capsule layer is proposed to aggregate high-level user preference from the low-level interaction history via a dynamic routing-by-agreement mechanism. Moreover, JTCN jointly trains the loss for mimicking the user preference and the softmax loss for the recommendation together in an end-to-end manner. Experiments on two publicly available datasets demonstrate the effectiveness of the proposed model. JTCN improves other state-of-the-art methods at least 7.07% for CiteULike and 16.85% for Amazon in terms of [email protected] in cold start recommendation.

With the prevalent application of Internet of Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for Quality of Service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study the QoS prediction in IoT environments, where contextual information is vital. In this article, we develop a holistic framework to attack the QoS prediction in the IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world data sets, and the experimental results verify the effectiveness of the proposed framework.

As the population ages, eldercare and healthcare have become major issues in recent years. Crossover healthcare services, instead of individual ones, have become the main form of service provision. In this work, a scenario-based requirement model (SBRM) is proposed for crossover healthcare service. A DSL and a prototype system are designed based on the model as well. Our model defines the requirements as: WHO, in what SCENARIOs, what PROCESSes need to be performed, and what RULEs need to be satisfied. We verify our model in the real case of the MEH (medical, eldercare, healthcare) crossover service. SBRM supports the service better in our cases and shows satisfactory efficiency, effectiveness, and reusability.

In recent years, many new business modes and strategies are constantly emerging in e-commerce. The new retail has been one of the most successful modes. Different from the traditional business modes, it is driven by information technology (big data, Internet of Things, artificial intelligence, etc.) and centered on consumer experience. Furthermore, it reconstructs the core elements in online and offline trade to form a new business mode. Thus, the existing business modeling approaches cannot be used to analyze and describe the new retail mode. In this article, we first analyze the business characteristics and processes in new retail and redefine the core elements in e-commerce, such as people, product, and place. EMB can decouple the business aspect and technical aspect of the business systems of e-commerce. So EMB can bridge the gap between business experts and application developers. In addition, we verify EMB by the electronic certificate business of Taobao and deeply analyze its reusability, applicability, and efficiency. Finally, to demonstrate the advantages of EMB, it is compared with some other existing methods in detail.

In the modern service industry, both service processes and data structures are becoming increasingly diverse and complex. In addition, interdependences exist among data, such that the use of "shoe size" data must be based on the "type of goods" data returning "shoe". This is also observed for the functions and interfaces in a system, as one can use the function "order payment" only after the function "order generation". This kind of phenomenon is rather common in service systems nowadays, especially when the service is a transboundary service such as the new retail proposed by Jack Ma. Traditional modeling methods have difficulties in handling such scenarios. There have been studies on service modeling over the past several years, and they have focused mainly on the service processes and interactions among services. In this work, we construct MeCo-TSM based on three sub-models to handle multi-entity complex service process. We verify our model in the real processes of our cooperation company and compare it with related works. MeCo-TSM supports the service better in our cases and shows satisfactory efficiency, effectiveness and reusability.

With the appearance of new retail, e-commerce has broken the traditional pattern, different categories of goods have their own unique business attributes.Taking transaction business as an example, the traditional physical goods business needs to complete the transaction through the logistics, while the new electronic voucher business achieves that by involving shop verification and the transaction will be totally completed after the consumption of the virtual goods such as the QQ coin.Traditional integral modeling that describes all businesses through a process has been hard to meet such a scenario.In recent years, there have been a lot of studies on business process modeling.These methods mainly focus on the process and data level and do not support business modeling well.In this work, we construct a Business Identification Model(BIM) based on four business sources to handle unique complex business process.We develop a platform based on BIM and verify our model in the real processes of our cooperation company.In addition, BIM supports assembling and reusing in business-level in our case.

As a new service paradigm of Software-as-a-Service (SaaS),Business Process-as-a-Service (BPaaS).BPaaS is used to build a cost-effective Business Process Management (BPM) system.Based on universal Artifacts, we develop a framework named SeGA (Self-Guided Artifact).In SeGA, a BPM system is capable of executing business processes from multiple clients, and responding query at runtime.what's more, by the template-based cascading data mapping method, entity to be synchronized with database automatically, so each BP instance can modify its process entity without worrying about the database access.In this paper, we conduct a deep research and implement a prototype system for Artifact process design.

Business process modeling is a way to the organizational change management, which provides abstract workflows to describe business logics for customer demands analysis and IT infrastructures improvement. In order to reuse business process for covering different application scopes, it requires the capacity of configuring each task of business process by selecting appropriate services from the candidate services set, and then assembling them together to properly work with each other as domain-specific software. Considering choosing an executable business process with high quality service (QoS), the service selection is regarded as the pivotal step of determining process instances since service displays probabilistic behaviors under the uncertainty of Internet making business process shown different reliability. In this paper, it proposes an approach to the service selection for business process modeling. In the first phase, the function similarity method is used to pick out services from service repository in order to build a set of candidate services, which checks the functions description to find matching services, especially service may publish one or more functions through multiple interfaces. In the second phase, the probabilistic model checking-based method is employed to the quantitative verification of process instances, which involves services composition and stochastic behaviors computing according to workflow structures. Then, corresponding algorithms are discussed for service selection purposes. Furthermore, it introduces the probabilistic model checking-based framework for prototype design and implementation. Finally, experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method comparing with traditional methods.

Service-Oriented Architecture (SOA) is a widely accepted and engaged paradigm for the realization of business processes that incorporate several distributed, loosely coupled partners. However, how to work with service computing in a cloud environment is the latest challenge. Formal methods can play a fundamental and important role in service computing and cloud computing. It has been great advances in formal methods research via tool support and industrial best practice, and their role in a variety of industries, domains, and in certification and assurance. The aim of FM-S&C 2016 is to encourage academic researchers and industry practitioners to present and discuss all formal analysis, modeling and verification related to research and experiences in a broad spectrum of services and cloud computing.

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.

Web service has been an important solution to achieve resource sharing and application integration in the Internet era, which can develop the most promising software application with the on-demand changing computing paradigm, through service reuse and dynamic synthesis. One of the latest challenges is how to work with service computing in a cloud computing environment. There is a strong tradition of attracting submissions on formal approaches to enterprise systems modeling in general, and business process modeling in particular. The topic of FM-S&C 2015 is the theory aspect of data intensive services and formal methods. It encourages academic researchers and industry practitioners to present and discuss formal analysis, modeling and verification related researches and experiences.

Emerging paradigm of cloud computing provides a new service delivery platform. One of the latest challenges is how to work with service computing in a cloud computing environment. Meanwhile, the convergence of service computing and cloud computing is becoming a major driving force for the adoption of both of these technologies. It has been great advances in formal methods research via tool support and industrial best practice, and their role in a variety of industries, domains, and in certification and assurance. Also, formal methods can play a fundamental and important role in service computing and cloud computing. The topic of FM-S&C 2014 is the theory aspect of data intensive services. There is no doubt in the industry and research community that the importance of data intensive computing has been raising and will continue to be the foremost fields of research. As a result, the data intensive services have become the important type of Web service. Also, it has become a hot issue in the academia and industry. Potentially, this could have a significant impact on the on-going researches for services and data intensive computing. The scope of the FM-S&C workshop series is not limited to technological aspects. In fact, there is a strong tradition of attracting submissions on formal approaches to enterprise systems modeling in general, and business process modeling in particular. Potentially, this might have a significant and lasting impact on the ongoing standardization efforts in cloud computing technologies. All papers accepted by the workshop are included in the proceedings of the IEEE 10th World Congress on Services (SERVICES 2014) which will be published by IEEE Computer Society. Hard copies may be obtained from IEEE Computer Society according to its ordering reprints policies. The electronic copies can be obtained from IEEE Xplore Digital Library.

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.

Service-based applications are typically based on dynamic reconfiguration, since it can be regarded as compositions of multiple Web services. Because service-based applications usually run in open, dynamic, ever-changing environments, providing function-consistent application becomes a big challenge. The paper proposes an effective dynamic reconfiguration approach for services based applications. The approach tries to replace each faulty service firstly. If the attempts fail, it will construct regions for each faulty service and tries to replace the region. In order to ensure the correctness of dynamic reconfiguration, we use services-behavioral type to formally describe services and propose subtype rule services-behavioral type to judge the substitutability between services. Also, the case study is given to show that how to verify the correctness of dynamic reconfiguration.

Dynamic reconfiguration can help SOA based applications to update, modify, add and remove their functions, improve their performance, enhance their reliabilities and robustness. However few works focus on the QoS-based dynamic reconfiguration of SOA-based applications. This paper presents an approach for QoS-based dynamic reconfiguration of SOA based applications. The proposed approach can reconfigure a SOA based application to comply with a new QoS constraint by replacing its individual or multiple component services. An important factor named global significance value is introduced to show the significance of each component service. The individual component services are attempted to replace according to the descending order relative to the value. If the attempts fail, multiple component services will be replaced together. In the case study, an example is given to show the approach is feasible to reconfigure a SOA based application to meet a new QoS constraint. The experiment shows the effectiveness and efficiency of our approach.

SOA based software is typically based on dynamic reconfiguration, since it is the composition of services. But few works focus on the non-functional reconfiguration of the SOA-based software. This paper presents an approach for QoS driven dynamic reconfiguration of the SOA based Software. The approach can reconfigure a SOA based software to comply with a new QoS constrains by replacing its individual or multiple component services. The individual component services are replaced according to the descending order relative to the critical factors. While if the attempts fail, multiple component services will be replaced together. In our case study, an example is given to show the approach is efficient to reconfigure a SOA based software to meet a new QoS constraints.

Consistency of Web services behavior is the key to ensure correctness and reliability of Web services choreography technology. In the paper, we introduce Martin-Löf type theory (MTT) and extend it to have a strong expressive capacity to describe formally Web services behavior. Based on this idea behind MTT, the paper applies extended-MTT to formally describe Web services behavior. Then, the rules of consistency are proposed based on combination of extended-MTT and type discipline. Next, the procedures of proofs are given that verify the consistency between behavior of vendor and behavior of vendor-s. In one word, our way is a suitable trade-off between expressiveness and amenability to efficiently verify.

Web services behavior is the key aspect to consistency of Web services, which can ensure correctness and reliability in Web services choreography. But few methods can conduct the trade-off between expressiveness and amenability of efficient verification, this paper gives a better answer to solve the problem. It constructs service-behavioral type discipline based on extended the Martin-Löf’s Type Theory (for short, MTT) which supports a type-theoretic formulation of services behavior structured patterns, so that services behavior in a distributed system can be verified by type checking. Then, the type rules for subtype, duality, and consistency of Web services behavior are discussed. The deductions are gave to show that how to verify the consistency between behavior of vendor and behavior of vendor-s.