Tracking the process of remote task execution is critical to timely process analysis by collecting the evidence of correct execution or failure, which generates a process digital twin (DT) for remote supervision. Generally, it will encounter the challenge of constrained communication, high overhead, and high traceability demand, leading to the efficient remote process tracking issue. Existing approaches can address the issue by monitoring or simulating remote task execution. Nevertheless, they do not provide a cost-effective solution, especially when unexpected situation occurs. Thus, we proposed a new cloud-edge collaboration framework for process DT generation. It addresses the efficient remote process tracking issue with a real-virtual collaborative process tracking (RVCPT) approach. The approach contains three patterns of real-virtual collaboration for tracking the entire process of task execution with a coevolution pattern, identifying unexpected situations with a discrimination pattern, and generating a process DT with a real-virtual fusion pattern. This approach can minimize tracking overhead, and meanwhile maintains high traceability, which maximizes the overall cost-effectiveness. With prototype development, case study and experimental evaluation show the applicability and performance advantage of the new cloud-edge collaboration framework in remote supervision.

Open set domain adaptation aims to transfer knowledge in the presence of unknown samples in the target domain. Previous approaches use additional classifiers or threshold-based methods to identify unknown samples and try to investigate the information of class diversity within the unknown samples. Despite achieving excellent adaptation results, these methods ignore those samples that lie on the cluster boundaries, especially the clustering-based methods. In this paper, we propose a novel Neighbor Prototype Contrastive Clustering (NPC2) method, which uses the Local Semantic Structure (LSS) to help these low-confidence samples located on the boundary of clusters to return to their own clusters. Further, we propose Local Semantic Consistency (LSC) to evaluate the clustering result and apply it to the domain adaptation process as a metric to assess the reliability of the samples. Results on four benchmarks show that our NPC2significantly outperforms most state-of-the-art methods with higher LSC.

Since 2020, the COVID-19 pandemic severely disrupted regular off-line business activities. This unprecedented situation inspires the valuable research on facilitating off-line business under pandemics. In this article, we conceptualized the problem as travel management in pandemic (TMiP) and analyzed it from the technological perspective. Enabling travel in a pandemic not only needs a health certificate to prove that the traveler is safe but also entry/exit permissions from both the origin and the destination regions, determined by the local situation and measures. Thus, TMiP is related to technical, social, economic, and administrative factors. By conducting a review on the literature covering the health certificate technology, its adoption in practice, and the exchange system technology published during the COVID-19 pandemic, we learned about their usefulness and limitations in TMiP. Second, we analyzed the review outcomes to infer the six distinctive technical challenges of TMiP. Third, we analyzed the feasibility of referential solutions to these challenges and showed their applicability and limitations. Finally, we offered the perspectives on new TMiP solutions and concluded that they rely on adapting existing solutions, creating new ones, and integrating all of them. We also presented future research directions in a holistic view of TMiP technical solutions. Overall, the findings of the study will stimulate more research on a more coordinated, comprehensive, and intelligent TMiP solution. We also hope this article can help practitioners to restart economies in a pandemic.

Recently, nonshared-and-imbalanced unsupervised domain adaption has been proposed to fix domain shift from Big Data source domain with long-tail distribution to specific small target domain with imbalanced distribution, including two challenges: 1) nonshared classes sharing in big data with long-tail distribution; and 2) imbalanced domain adaptation. Prior approaches explore knowledge sharing between classes to improve performance of unsupervised domain adaption methods. However these methods have inductive bias for prior tree or graph. And previous contrastive domain adaptation methods take center-based prototypes as positive samples which only coarsely characterize the domain structure, and fail to depict the local data structure. To fix these problems, we propose a novel framework called contrastive memory feature transfer (CMFT). To solve nonshared data sharing without inductive bias, we build a centroid memory baseddirected memory transfermechanism to enhance imbalanced class features with similar nonshared class centroid. To address the imbalanced domain adaptation, we design a fault-tolerant and fine-grainedneighborhood prototypefor the contrastive learning which can narrow the domain shift. The proposed CMFT outperforms previous methods on most benchmarks.

Existing methods consider Word Segmentation (WS) as sequence tagging. Each tag indicates the position of the current character in a segment. The exactness of the position for any non-boundaries character is unnecessary. Any incorrect inner prediction reduces model performance. The position information restricts tag-to-tag transition. Thereby, extra context information and the Conditional Random Field (CRF) network are desired to control unreasonable tag transition. To steer away from the implicit restriction, we propose the Separation(Sp)-Adhesion(Ad), which targets straight on the essential character-to-character connections, to tackle the WS task directly. Merely bigram that is specially tailored for “Sp-Ad” is required and considered as the processing unit to identify the connection states of every two adjacent characters. The elimination of the position restriction makes the model independent of the CRF layer which is widely adopted to revise unreasonable tags. Therefore, CRF can then be substituted with a classification network. We construct the Separation Inference (SpIn) framework based on the bigram features and softmax classification network to tackle the WS task. SpIn significantly reduces the inference complexity, dispels extra context information, and boosts the accuracy of the WS task. Besides its effectiveness in Chinese Word Segmentation, performance boosts on Japanese and Korean Word Segmentation further prove SpIn is universal for East Asian Languages. Moreover, our extensive experiments also verify the cross-domain effectiveness of SpIn by attaining state-of-the-art performances in the benchmark tests of in-domain and cross-domain Chinese Word Segmentation.

In this article, we present a novel solution to automating the procurement of food materials by using electronic scales, which can automatically identify the food materials along weighing them. Although the CNN model is regarded as one of the most effective solutions to image recognition, the traditional techniques cannot handle the mismatch problem between the lab training data and the real world data. To solve the problem, we propose to embed a partial-and-imbalanced domain adaptation technique (tree adaptation network) in the deep learning model, which can borrow knowledge from sibling classes, to overcome the imbalance problem, and transfer knowledge from the source domain to the target domain, to fight the mismatch problem between the lab training data and the real world data. Experiments show that the proposed approach outperforms state-of-the-art algorithms. Furthermore, the proposed techniques have already been used in practice.

Multi-agent collaboration in the agent oriented Collective Adaptive Systems could offset the failure of goal achievement by an independent agent. Commitments among agents to meld collaborative patterns typically defined in design time, which may cannot service the request of ACSs in response to changes in the system itself or in its environment. To address this problem, we propose a Goal-Capability-Commitment (GCC) based multi-agent collaboration mediation for ACSs at run-time. There are two issues with this state of the art approach: (1) the ability to represent the capability of agent under different contexts and diagnose the invalids of goals and agents leaded by the changing environments, and (2) the ability of mediation to select an appropriate capability or generate a new commitment in order to satisfy adaptive goal. We illustrate our approach through two experiments over the simulated scenario of automated hospital cart transportation system and discuss the results of experiments.

With the development of mobile technology, mobile virtual worlds have attracted massive users. To improve scalability, a peer‐to‐peer virtual world provides the solution to accommodate more users without increasing hardware investment. In mobile settings, however, existing P2P solutions are not applicable due to the unreliability of mobile devices and the instability of mobile networks. To address the issue, a novel infrastructure model, called Virtual Net, is proposed to provide fault‐tolerance in managing user content and object state. In this paper, the key problem, namely, object state update, is resolved to maintain state consistency and high interaction responsiveness. This work is important in implementing a scalable mobile virtual world.

Heterogeneous device services generated by various devices in different contexts prevent users from efficiently and correctly consuming device services. This seriously hinders the development of Internet of Things. This paper addresses the problems appearing in device discovery and device interaction. It devises a user interoperability framework (UIF) to enable device users to interoperate with heterogeneous devices of different contexts with consistent syntax and semantics. In this framework, a new separation strategy is provided; a device representation method for real, common, and virtual devices is devised; and a device transformability model is proposed to guarantee the proper transformation of device syntax and semantics. To demonstrate the correctness of UIF, a UIF prototype is implemented and several experiment methods are compared to determine which one should be adopted as semantic relatedness computing tools in device discovery for device users and in common device publishing for device providers.

An important research problem in designing an open virtual world is how to enable virtual currency exchange between multiple virtual worlds and guarantee the exchange fairness when multiple virtual currencies are adopted as virtual payment instruments in virtual trades between virtual worlds. While an existing VMX theory solved the fairness problem in a VERA algorithm based on a Pareto exchange point [3], a new expecation problem has been found such that exchange requesters might submit minimum acceptable rates (MAR) to determine whether to accept VMX systems-generated virtual currency exchange rates. When exchange requestors think the systems-generted rates are lower than MAR, they select not to accept the systems-generated rates. The withdrawal of systems-generated rates creates the expectation problem such that the fairness has been lost due to Pareto exchange point no longer exists. To solve the expectation problem, this paper has developed a new VERA-RS algorithm by extending the existing VERA algorithm based on a newly developed formal expectancy model and a novel m-stage and n-phase three-level computing framework. VERA-RS algorithm has solved expectation problem by achieving a set of satisfied virtual currency exchange rates.

In this paper, we propose a novel automatic image annotation model by mining the web. In our approach, the terms or words appearing in the associated text are extracted and filtered as labels or annotations for the corresponding web images. Sure, much noise exists in those selected labels. In order to reduce the influence caused by the noisy labels, for each label or potential word, we improve web image-word relationships using Mixture Gaussian Distribution Model. By doing so, the relationships between words and images are re-weighted both in terms of sematic relevance and in terms of visual feature similarity. In fact, all the words associated to an image are not semantically independent. We use co-occurrences between two words to describe their semantic relevance. Thus, we further use a method, called Word Promotion, to co-enhance the weights of all the words associated to a given image based on their co-occurrences. Our experiments are conducted in several ways and the results show that our annotation method can achieve a satisfactory performance in respects of system scalability and sematic evolution.

For the multilingual semantic interoperations in cross-organizational enterprise systems and e-commerce systems, semantic consistency is a research issue that has not been well resolved. This paper contributes to improving multilingual semantic interoperation by proposing a concept-connected near synonym (NSG) framework for concept disambiguation. NSG framework provides a vocabulary preprocessing process of collaborative vocabulary editing, which further ensures semantically consistent vocabulary for building semantically consistent business processes and documents between context-different information systems. The vocabulary preprocessing offered by NSG automates the process of finding potential near synonym sets and identifying collaboratively editable near synonym sets. The realization of NSG framework includes a probability model that computes concept values between concepts based on a newly introduced semantic relatedness method-SRCT. In this paper, SRCT-based methods are implemented and compared with some existing semantic relatedness methods. Experiments have shown that SRCT-based methods outperform the existing methods. This paper has made an improvement on the existing methods of semantic relatedness and reduces the collaboration cost of collaborative vocabulary editing.

An electronic marketplace (e-marketplace) is a common business information space populated with many entities of different system types. Each of them has its own context of how to process activities. This leads to heterogeneous e-marketplace activities, which are difficult to make interoperable and inferred from one entity to another. This study solves this problem by proposing a concept of separation strategy and implementing it through providing a semantic inference engine with a novel inference algorithm. The solution, called the RuleXPM approach, enables one to semantically infer a next e-marketplace activity across multiple contexts/domains. Experiments show that the cross-context/cross-domain semantic inference is achievable. This paper is an understanding of many aspects related to heterogeneous activity inference.

In e-business development, semantics-oriented document exchange is becoming important, because it can support cross-domain user connection, business transaction and collaboration. To provide this support, this paper proposes a DOC Mechanism to exchange semantically interoperable business documents between heterogeneous enterprise information systems. This mechanism is designed on a layered-sign network, which enables any exchanged e-business document to be independently interpretable without losing semantic consistency.

Establishing an open virtual money exchange regime is a novel idea but rarely discussed. This paper provides a pioneer research on virtual money exchange (VONEX) approach, aiming to facilitate the exchange of virtual currencies among a variety of heterogeneous virtual communities. It has defined the intrinsic value and exchangeable value of virtual currencies and suggested a redistribution strategy, constituting the theoretical foundation of the virtual exchange rate system, by which a novel VONEX exchange rate algorithm is derived. Further to the introduction of the algorithm, a proof is given to ensure its correctness.

In this paper, we provide a novel image annotation model by mining the Web. In our approach, the concepts or words appearing in the associated text are extracted and filtered as the semantic annotations for the corresponding Web images. In order to alleviate the influence caused by the noise images, for each semantic concept, we improve Web image-word relationships using Mixture Gaussian Distribution Model. By doing so, the concepts or words relevant to any image are re-weighed by both considering their relevance to the image in term of text and in term of visual feature. In fact, all the words associated to an image are not semantically independent. We use co-occurrences between two words to describe their semantic relevance. Thus, we further use a method, called Word Promotion, to co-enhance the weights of all the words associated to a given image based on their co-occurrences. Our experiments are conducted in several ways and the results show that our annotation method can achieve a satisfactory performance.

An analysis on the existing e-marketplaces shows there are seven types of technical construction methods for e-marketplaces. They are e-catalogue, e-shop, e-portal, e-hub, e-switch, e-integrator and e-merger. The quality of these e-marketplaces can be measured based on a quality matrix of accuracy, reach and richness.

E-marketplace activities initiated by users in general require representing the user requirements and preferences matched with a set of offerings. One issue of these activities is the heterogeneity among them, which asks for semantic consistency maintenance. This paper solves this problem by applying collaborative concept exchange technology and developing a novel RuleXPM approach. This approach transforms XPM reified documents into rule-based RuleXPM documents that are suitable for making cross-domain inference on heterogeneous e-marketplace activities, using defeasible reasoning on a newly designed RuleXPM inference engine made from a RuleXPM inference algorithm.

As World Wide Web is becoming the most important information repository, increasing amount of information is available. Currently, web search engines can only provide document oriented searches. In order to fully make use of information from the web, some effective and efficient extraction algorithms are definitely desirable. In this paper, some existing achievements are investigated firstly. Then our current technique on web information extraction is discussed in detail. In our approach, rules and patterns are extracted from sample pages through training process, with human involvements. We use both keywords and regular expressions to represent rules and patterns in our system. The keywords work as anchors to locate the positions of the potential information and regular expressions work as validations of the values. In our system, all the extracted information is represented in XML format.

In this paper, we studied the semantic consistency maintenance issue between heterogeneous contexts, that is, how a firm ‟ busi-ness process of one e-marketplace can be transformed to another firm‟s business process of another e-marketplace in a semantically consistent way. The proposed solution of this paper uses XML Product Map (XPM) of collaborative concept to represent semantically consistent business processes, and adopts common action concept pool and XPM documents to design heterogeneous business processes that are suitable for heterogeneous business process transformation. We motivated the approach with a real-world PVC poncho trade problem and explained it in architecture of collaborative process design and automatic service provision. We reported the implementation specification within a hybrid human-agent framework, where four layers of system modules are specified. The approach is evaluated based on a new semantic impact chain method particularly for evaluating concept consistency in meaning representation between heterogeneous contexts of business processes.

Electronic business document interoperation is the cornerstone of business process integration. An essential issue for business document interoperation is to maintain semantic consistency of the exchanged business documents between any two autonomous business communities, where the document sender and receiver have no misunderstanding in using the exchanged documents. Existing approaches to resolving this issue either adopts document standards to map heterogeneous document elements or applies business ontologies to mediate inconsistent document elements. While these approaches are effective in certain degree, the issues of limited flexibility and evolvability in using standards and the lack of accuracy in using ontologies to mediate document elements must be explored and resolved. This paper proposes a Collaborative Document Exchange (CODEX) approach to resolving the issues. In this approach, structures and concepts of business document are separated and layered in CODEX framework. Structures provide the commonality of business documents through classified concept identifiers while concepts support particularity of business documents through collaboration.

An unfavorable phenomenon is observed: current electronic markets are fragmented and have formed a set of autonomously distributed product information islands. This leads to heterogeneity of product information between separated sources and makes difficult on product document interoperation. To resolve the issue and facilitate business document interoperation, this paper proposes a heterogeneous concept mapping approach. By this approach, heterogeneous product documents are transformed from one context to another context without losing any semantic information. This transformation process is supported by a heterogeneous concept transformation algorithm that includes five transformation steps: source-local context transformation, local-common context transformation, common-common context transformation, common-local context transformation and local-source context transformation.

Product data exchange is the precondition of business interoperation between Web-based firms. However, millions of small and medium sized enterprises (SMEs) encode their Web product data in ad hoc formats for electronic product catalogues. This prevents product data exchange between business partners for business interoperation. To solve this problem, this paper has proposed a novel concept-centric catalogue engineering approach for representing, transforming and comparing semantic contexts in ad hoc product data exchange. In this approach, concepts and contexts of product data are specified along data exchange chain and are mapped onto several novel XML product map (XPM) documents by utilizing XML hierarchical structure and its syntax. The designed XPM has overcome the semantic limitations of XML markup and has achieved the semantic interoperation for ad hoc product data exchange.