Current crop disease VQA models primarily focus on object counting and detection. However, accurately identifying various disease stages and determining control measures re quire additional knowledge beyond images, including information about control methods and pathogen details. To address this, the VQA dataset relies on the images and questions to retrieve relevant external knowledge. To realize the VQA task of crop diseases external knowledge, we construct the Visual Question Answer Model Based on Crop Diseases External Knowledge for Smart Agriculture (CDEK). CDEK integrates two categories of external knowledge on 66 common dicotyledonous crop diseases by utilizing large language models and agricultural knowledge repositories to enhance knowledge retrieval. This integration enhances the richness of external knowledge repositories. En hancing fine-grained image understanding in CDEK through the utilization of Stack Self-Attention (SSA), utilising Cross Attention and contrastive learning of two external knowledge, with a focus on emphasizing image-related semantic information during training. Finally, an automatic patrol disease detection robot is constructed based on Tensor Processing Unit (TPU) devices and the CDEK model. CDEK achieves an accuracy of 61.7% on the publicly available dataset OK-VQA, surpassing the previous state-of-the-art by 5.1%. Furthermore, we construct the OKiCD-VQA dataset for crop diseases external knowledge and achieve an accuracy of 89.36% using CDEK. A series of ablation experiments are conducted on various modules, the effectiveness of CDEK is demonstrated through extensive experimentation. Contributing solutions to the sustainable development of smart agriculture.

The recommendation based on dynamic graph sequences aims to reveal complex evolutionary patterns in user-item interactions. Existing methods make predictions by encoding attribute contents through similarity but lack dynamic modeling of fine-grained attribute scenarios, resulting in a deviation in user interest representation. To address above issues, we propose a novel Dynamic Graph multi-granularity Attribute Scene evolution sequence Recommendation (DGASR) to enhance content features and reduce user interest bias by finer granularity modeling dynamic attributes. Firstly, we design an attribute-aware reconstruction module to model attribute interest distribution to reconstruct attributes and graphs. Subsequently, we design an attribute-aware long-short term module. It enhances long-term evolution characteristics of user behavior under attribute scene changes and constrains the consistency of users’ short-term interest distribution, achieving dynamic modeling of behavioral preferences under attribute scene distribution. Finally, DGASR achieves state-of-the-art results on three benchmark datasets, significantly outperforming several typical cold-start methods.

Heterogeneous Graph Neural Networks(HGNNs) are widely regarded as an effective tool for modeling data with graph structures in recommendation. Current research lacks modeling of user attribute and project attribute distribution preferences, limiting graph structure optimization potential. In response to these challenges, we propose a novel Heterogeneous Graph Dual-structure Optimization based Attribute-aware for Recommendation systems (HDSAR). It captures users’ personalized preferences through attribute-aware enhancement and uses dual-structure optimization to improve recommendation performance. First, we design an attribute-aware enhancement module to significantly enhance the relevance of attributes between users and items. Second, we use attribute-aware signals to explicitly filter heterogeneous neighbor ranges to preserve high- quality structural neighborhoods. Then, we employ contrastive learning to enhance the consistency of attribute-aware signals and heterogeneous structures to implicitly optimize the structural learning. Experiments on two real-world datasets demonstrate that HDSAR’s recommendation performance surpasses that of state-of-the-art methods.

Request for Comment (RFC) is a universal and standardized specification and describes internet protocol processes, algorithms, and standards. The formal representations of network protocols extracted from RFC can be used to verify correctness and security of the communication process. This paper proposes automatically extracting Petri net models from RFC protocol texts, transforming unstructured protocol text into a structured model. First, the RFC protocol text is preprocessed with chunking, part-of-speech, and element labeling. Second, element recognition is performed by the trained Bert-DGCNN-Bi-LSTM-CRF network, and a structured intermediate representation is generated based on the recognition results and semantic role relationships. Then, state transfer relations are extracted from the intermediate representation and stored in the correlation matrix. The correlation matrix is converted to a PNML file for visualization of Petri nets using PIPE software. Finally, an experimental comparative analysis of element recognition and relation extraction is conducted to prove the effectiveness of the extraction approach. Comparing the similarity between automatic and manual model extraction proves the quality of the extracted models.

The Gate Recurrent Unit (GRU) has a large blank in the application of legal transition sequences for bounded Petri nets. A GRU-based method is proposed for the recognition of bounded Petri net legal transition sequences. First, in a Petri net, legal and non-legal transition sequences are generated according to a certain noise ratio. Then, the legal and non-legal transition sequences are inputted into GRU to recognize the legal transition sequences by encoding the maximum variation sequence length with a uniform length. The proposed method is validated with different Petri nets at different noise ratios and compared with seven widely-known baselines. The results show that the proposed method achieves excellent recognition accuracy and robustness in most situations. Solving the problem that the existing methods cannot recognize the legal transition sequences of Petri nets in real time.

Business process similarity measures are of vital importance for process repository management applications, such as process query, process recommendation, and process clustering. Most existing approaches measure process similarity by relying on control-flow structures only. This article investigates the role of data in process similarity measure. To incorporate data-flow information into business process control flow, it proposes a data-aware workflow net (DWF-net) by extending the classical workflow net with data reading and writing semantics. Then, we introduce three types of similarity measures, i.e., data item set-based similarity, data operation set-based similarity, and data-aware behavior-based similarity, to quantify the similarity of data-aware business processes from different perspectives. Next, a methodology is introduced to help process analysts apply these three measures in a systematical way. Finally, we evaluate the effectiveness and applicability of the proposed similarity measures by a group of comparative experiments.

Summary Modern information systems are continuously collecting and storing large volumes of business process event logs. The analysis of event logs can provide valuable insights for business process re‐engineering and enhancement. Process discovery, as one of the most challenging event log analysis techniques, aims to discover a business process model from an event log. Many process discovery approaches have been proposed in the past two decades, however, most of them suffer from efficiency problem when dealing with large‐scale event logs. Motivated by PageRank, we propose LogRank, a graph‐based ranking model, for event log sampling in this paper. The LogRank is capable of sampling a large‐scale event log to a smaller size that can be efficiently handled by existing discovery approaches. To support real‐life applications, we instantiate the LogRank model for two typical types of event logs, that is, simple event logs and lifecycle event logs. To quantify the quality of a sample log with respect to the original one, we introduce a general evaluation framework that can be instantiated for different quality metrics. The proposed sampling approach has been implemented in the open‐source process mining toolkit ProM. By experiments with both synthetic and real‐life event logs, we demonstrate that the proposed LogRank‐based sampling approach provides an effective means to improve process discovery efficiency as well as guaranteeing high quality of discovered models.

Workflow management technology has become a key means to improve enterprise productivity. More and more workflow systems are crossing organizational boundaries and may involve multiple interacting organizations. This article focuses on a type of loosely coupled workflow architecture with collaborative tasks, i.e., each business partner owns its private business process and is able to operate independently, and all involved organizations need to be synchronized at a certain point to complete certain public tasks. Because of each organization’s privacy consideration, they are unwilling to share the business details with others. In this way, traditional correctness verification approaches via reachability analysis are not practical as a global business process model is unavailable for privacy preservation. To ensure its globally correct execution, this work establishes a correctness verification approach for the cross-organizational workflow with task synchronization patterns. Its core idea is to use local correctness of each suborganizational workflow process to guarantee its global correctness. We prove that the proposed approach can be used to investigate the behavioral property preservation when synthesizing suborganizational workflows via collaborative tasks. A medical diagnosis running case is used to illustrate the applicability of the proposed approaches.Note to Practitioners—Cross-organizational workflow verification techniques play an increasingly important role in ensuring the correct execution of collaborative enterprise businesses. This work addresses the issue of correctness verification for loosely coupled interactive workflows with collaborative tasks. To ensure the globally correct execution, a behavioral correctness verification approach is established. All proposed concepts and techniques are supported by open-source tools, and evaluation over a medical diagnosis process case has shown their applicability. The proposed methodology is readily applicable to industrial-size workflow correctness verification problems.

Accurately estimated data transmission ability is important in operating a vehicular ad-hoc network (VANET), which has limited bandwidth and highly dynamic typology. The mobility behavior of traditional wireless networks is different from VANET's, and existing results on the former are not applicable to VANET directly. Most existing studies on VANET capacity estimation focus on asymptotic descriptions. In them, messages sent and received by vehicle nodes are composed of data packets, and vehicle nodes can move along roads only. In this paper, a modeling and calculation approach for accurate VANET capacity is proposed. We transfer vehicle nodes to data packets and then abstract data packets that can move along roads into data flow in virtual pipelines. Then, we derive a fluid mechanics-based data flow model and propose capacity calculation equations. According to network scale, network capacity is divided into following three stages: linear growth, maintenance, and decline. This paper demonstrates that the data flow model-based capacity is consistent with that of simulation results.

When an emergency occurs, one of the important challenges is how to form an effective and timely response. An emergency disposal plan is usually organized as a series of emergency response processes manipulated by one emergency command center and several subordinate emergency organizations. Moreover, these subordinate organizations are usually geographically dispersed and need to collaborate with each other. In this case, designing and verifying such cross-organizational collaborative emergency response processes are complicated and time-consuming. To address this problem, we propose a hierarchical modeling and correctness verification approach. A general framework for hierarchical modeling and correctness verification of such processes is first introduced. Then, a top-level model and two kinds of bottom-level models (complex and simple bottom-level ones) are proposed to model such processes and collaboration patterns from different abstraction levels. Next, Petri net refinement operation is adopted to refine the top-level model by using its corresponding bottom-level models to obtain the refined model. Finally, the correctness of the refined model is verified based on reachability graph. A typical running case of cross-organization collaborative fire emergency response processes is given to validate our proposed method.

A group of geographically dispersed and logically collaborated emergency organizations are involved when emergency occurs. Two challenging issues are a reasonable emergency resource allocation mechanism and an efficient resource conflict checking and resolution control mechanism to ensure the conflict-free execution of global cross-organization emergency response processes. To address them, this paper proposes an approach to support emergency resource management including both intraorganization private resource management and cross-organization public resource management. The former has been fully discussed in our previous work using E-net. To cope with the latter, this paper presents a novel type of Petri net that is extended with both time and resource factors to model cross-organization emergency response processes. Then, according to the resource requirement analysis, their worst, delayed, and best cases are obtained. Next, emergency resource conflict checking and four conflict resolution strategies are proposed to resolve the detected resource conflicts for the delayed execution case. This paper shows how to use different resolution strategies to construct the conflict-free model by designing corresponding resolution controllers. Finally, their performance is evaluated by a fire emergency response example.

More and more business requirements are crossing organizational boundaries. There comes the cross-organization business process management, and its modeling is a complicated task. Mining a cross-organization business process aims to discover its model from a set of distributed event logs. Unfortunately, traditional process mining approaches totally neglect the privacy-preservation issue, which means the privacy of both event log and business process model. In this paper, a privacy-preservation cross-organization business process mining framework is proposed to handle its privacy issues. It includes three steps: (1) each organization discovers its private and public business process models from its event logs; (2) the trusted third-party midware takes the public process models as input and generates cooperative public process model fragments of each organization; and (3) each organization combines its private business process model with its relevant public fragments to obtain the organization-specific cross-organization cooperative business process model. To illustrate the applicability of the proposed approach, a multi-modal cross-organization transportation case is used for its validation and comparison with other methods.

Deadlock detection plays an important role in the analysis of system behavior. Several kinds of reachability trees have been proposed to analyze Petri net properties including deadlock freedom. However, existing reachability trees can only solve the deadlock detection problem of bounded or some special kinds of unbounded Petri nets. To increase the applicable scope of reachability trees in the deadlock detection field, this paper presents a new type of reachability trees called complex reachability tree (CRT). Different from others, transition sequences corresponding to root-started paths of cyclic CRTs are always firable from the initial marking. The proposed trees can completely solve the deadlock detection problem of ω-gone node-free Petri nets, but cannot guarantee to detect all the deadlocks for the other kinds of unbounded Petri nets. Their construction method and applications are presented.

Traditional process discovery techniques mine process models based upon event traces giving little consideration to workflow relevant data recorded in event logs. The neglect of such information usually leads to incorrect discovered models, especially when activities have decomposable cyclic dependencies. To address this problem, the recorded workflow relevant data and decision tree learning technique are utilized to classify cases into case clusters. Each case cluster contains causality and concurrency activity dependencies only. Then, a set of activity ordering relations are derived based on case clusters. And a synchronization-core-based process model is discovered from the ordering relations and composite cases. Finally, the discovered model is transformed to a BPMN model. The proposed approach is validated with a medical treatment process and an open event log. Meanwhile, a prototype system is presented.

Time and resource management and optimization are two important challenges for an emergency response process, by which all individuals and groups manage hazards in an effort to avoid or ameliorate the impact of disasters. Compared with a traditional business process, an emergency response process has its own features. To our best knowledge, there is no formal method to model and analyze emergency response processes by taking uncertain activity execution duration, resource quantity, and resource preparation duration into account. This paper presents such a method based on an E-Net that is a Petri net-based formal model for an emergency response process constrained by resources and uncertain durations. According to the number of available resources, execution of an E-Net is classified into the worst, delayed, and best cases. Based on a priority-activity-first strategy and corresponding algorithms, this paper finds the duration to execute each activity for the delayed case. By experiments, we prove that the proposed strategy can ensure shorter execution duration of the whole process than a conventional one. A running case of a chlorine tank explosion is given to validate the proposed method.

Recently, cross-department business processes are becoming more and more complex. Different kinds of coordination patterns exist among different departments, which make modeling and analysis work more difficult. To our best knowledge, there is no formal method to give systematic modeling and verification for the cross-department processes when considering different kinds of coordination patterns among different departments. This paper proposes such a method based on Petri nets. The WF-net model extended with resource and message factors, RM_WF_Net for short, is first introduced. Then, the formal model of tasks is proposed and its coordination relations are given. Next, RM_WF_Net modeling for intradepartment processes is investigated and cross-department coordination patterns, including message interaction pattern, resource interaction pattern, task collaboration pattern, procedure abstract, service outsourcing pattern, and process activation pattern, are formally defined. The soundness of the RM_WF_Net is verified based on the reachability graph. A running case of the cross-department medical diagnosis business process is given to validate our proposed method.

Petri nets are suitable for modeling and analysis of business processes. However, the lack of data concepts often makes Petri-net-based models excessively large and difficult to analyze especially when process logic is sensitive to changes of process attribute values. Emergency treatment processes are a typical example of this situation. To solve the aforementioned problems, this paper proposes a new hierarchical Petri net model for modeling and verification of emergency treatment processes. The hierarchical Petri net model includes three separate but closely related models, i.e., a business process logic net, a business process semantic net, and a set of case models. Business process logic nets are used to model the task dependencies disregarding semantic information. Business process semantic nets introduce data concepts to business process logic nets to model semantic information such as process attributes or conditions of sequence flows. Case models are used to model the practical routes of specific business instances. Based on the three models, a formal verification algorithm of an emergency treatment process is presented. Finally, the hierarchical modeling and verification methods are validated by an emergency treatment process of highways under snow/ice weather conditions.