In recent years, Relational Triple Extraction (RTE) methods leveraging Large Language Models (LLMs) have garnered lots of attention. Several studies attempted to harness ontology, the foundational template of knowledge graph, to assist LLMs in comprehending the structure of knowledge graph. However, the broadness of ontology within the prompts leads to unsatisfactory outcomes for RTE tasks. To address this, we proposed an Ontology-based Graph and LLM Fusion method for Relational Triple Extraction (OGLRTE). In this framework, we pioneeringly designed a two-stage RTE method centered around ontology, comprising of a relation filter and a text generator. To fully utilize the information in knowledge graph, we proposed an innovative combination of the ontology and co-occurrence graph within relation filter to repersent co-occurrence of relations in knowledge graph. Additionally, we employed established fine-tuning techniques to optimize the LLM within our triple generator, thereby enhancing its capability to extract triples. Our method surpasses traditional methods and LLM-based methods in extracting information, as evidenced by its superior performance on three public datasets: DocRED, NYT10 and CoNLL04.

Polynomial-based learnable spectral graph neural networks (GNNs) utilize polynomial to approximate graph convolutions and have achieved impressive performance on graphs. Nevertheless, there are three progressive problems to be solved. Some models use polynomials with better approximation for approximating filters, yet perform worse on real-world graphs. Carefully crafted graph learning methods, sophisticated polynomial approximations, and refined coefficient constraints leaded to overfitting, which diminishes the generalization of the models. How to design a model that retains the ability of polynomial-based spectral GNNs to approximate filters while it possesses higher generalization and performance? In this paper, we propose a spectral GNN with triple filter ensemble (TFE-GNN), which extracts homophily and heterophily from graphs with different levels of homophily adaptively while utilizing the initial features. Specifically, the first and second ensembles are combinations of a set of base low-pass and high-pass filters, respectively, after which the third ensemble combines them with two learnable coefficients and yield a graph convolution (TFE-Conv). Theoretical analysis shows that the approximation ability of TFE-GNN is consistent with that of ChebNet under certain conditions, namely it can learn arbitrary filters. TFE-GNN can be viewed as a reasonable combination of two unfolded and integrated excellent spectral GNNs, which motivates it to perform well. Experiments show that TFE-GNN achieves high generalization and new state-of-the-art performance on various real-world datasets.

Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, neighborhood awareness remains essential and challenging for GCNs. Existing methods usually perform neighborhood-aware steps only from the node or hop level, which leads to a lack of capability to learn the neighborhood information of nodes from both global and local perspectives. Moreover, most methods learn the nodes' neighborhood information from a single view, ignoring the importance of multiple views. To address the above issues, we propose a multi-view adaptive neighborhood-aware approach to learn graph representations efficiently. Specifically, we propose three random feature masking variants to perturb some neighbors' information to promote the robustness of graph convolution operators at node-level neighborhood awareness and exploit the attention mechanism to select important neighbors from the hop level adaptively. We also utilize the multi-channel technique and introduce a proposed multi-view loss to perceive neighborhood information from multiple perspectives. Extensive experiments show that our method can better obtain graph representation and has high accuracy.

Unbounded Petri nets (UPNs) can describe and analyze discrete event systems with infinite states (DESIS). Due to the infinite state space and the combination explosion problem, the reachability analysis of UPNs is an NP-Hard problem. The existing reachability analysis methods cannot achieve an accurate result at reasonable costs (computational time and space) due to the finite reachability tree with$\omega$-numbers. Based on the idea of approximating infinite space with finite states, given some limited reachable markings of a UPN, we propose a method that can quantitatively solve the UPN’s reachability problem with machine learning. Firstly, we define the probabilistic reachability of markings and transform the UPN’s reachability problem into the prediction problem of markings. The proposed method based on positive and unlabeled learning (PUL) and bagging trains a classifier to predict the probabilistic reachability of unknown markings. Finally, to predict the markings outside the positive sample set and unlabeled sample set, an iterative strategy is designed to update the classifier. Based on seven general UPNs, the results of the experiments show that the proposed method has a good performance in the accuracy and time consumption for the UPN’s reachability problem.Note to Practitioners—In discrete event systems, the reachability problem mainly studies reachable states of the system and the relationship between states, which is the basis of the system’s states, behaviors, attributes and performance analysis. For discrete event systems with infinite states, it is hard to analyze the reachable relationship between states within a finite time due to the infinite state space and the combination explosion problem. The main motivation of the paper is to propose a method that can predict the reachable relationship between the states with a probability value within a finite time. By machine learning algorithms, the method learns the feature information of the known reachable states. The reachability of unknown states in the infinite state space can be predicted approximately. The proposed approximation method can be applied to analyze the reachability properties of general discrete event systems with infinite states, such as checking whether a fault occurs in operating systems, whether a message is delivered in communication and so on.

Effective Service Function Chains (SFCs) mapping and Virtual Network Functions (VNFs) scheduling are crucial to ensure high-quality service provision for Internet of Things (IoT) tasks. Meeting the varying demands of multiple SFCs poses a significant challenge, particularly when working with the limited resources available in edge computing networks. Most existing working focuses on uniformly mapping and scheduling service requests in a batch processing manner within a given time period, without taking the diversity and priority of VNFs into account. When there is a sudden surge in demand, the issues of VNF queueing waiting and resources imbalance become prominent. To address the mentioned issues, this paper proposes a Deep Reinforcement Learning (DRL)-based VNF cooperative scheduling framework with priority-weighted delay. In light of the urgency of VNFs with higher priorities and the limitations of available resources, we begin by modeling an average queuing delay with priority weight based on the shortest remaining time priority technique. We then formulate a mathematical optimization problem to minimize the modeled delay in VNF scheduling process while providing suitable multidimensional resources in the edge network. Finally, a DRL method with experience replay and target Q-network is designed to effectively obtain the optimal solutions of the optimization problem from experience. The experimental results show that our proposed method outperforms its peers in terms of SFC request acceptance, delay, load balance, and resource utilization.

Graph convolutional networks (GCNs) have shown superior performance on graph classification tasks, and their structure can be considered as an encoder-decoder pair. However, most existing methods lack the comprehensive consideration of global and local in decoding, resulting in the loss of global information or ignoring some local information of large graphs. And the commonly used cross-entropy loss is essentially an encoder-decoder global loss, which cannot supervise the training states of the two local components (encoder and decoder). We propose a multichannel convolutional decoding network (MCCD) to solve the above-mentioned problems. MCCD first adopts a multichannel GCN encoder, which has better generalization than a single-channel GCN encoder since different channels can extract graph information from different perspectives. Then, we propose a novel decoder with a global-to-local learning pattern to decode graph information, and this decoder can better extract global and local information. We also introduce a balanced regularization loss to supervise the training states of the encoder and decoder so that they are sufficiently trained. Experiments on standard datasets demonstrate the effectiveness of our MCCD in terms of accuracy, runtime, and computational complexity.

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus, automatically detect a fraudulent transaction. We propose a novel model by improving long short-term memory with a time-aware gate that can capture the behavioral changes caused by consecutive transactions of users. A current-historical attention module is designed to build up connections between current and historical transactional behaviors, which enables the model to capture behavioral periodicity. An interaction module is designed to learn comprehensive and rational behavioral representations. To validate the effectiveness of the learned behavioral representations, experiments are conducted on a large real-world transaction dataset provided to us by a financial company in China, as well as a public dataset. Experimental results and the visualization of the learned representations illustrate that our method delivers a clear distinction between legitimate behaviors and fraudulent ones, and achieves better fraud detection performance compared with the state-of-the-art methods.

The explosive development of the Internet of Things (IOT) has stimulated the sudden and dynamic demand pattern of potential Service Function Chain (SFC) traffic. The virtual network built based on the requirements of Multiple SFCs (Multi-SFCs), sharing the resource, should provide better Quality of Experience (QoE) for Multi-SFCs according to the real-time network status. However, simultaneous Multi-SFCs in the routing process compete for the VNF resources of the same node and preempt the same link. The instability of QoE caused by node invalidation and link faults is highlighted. Therefore, to provide a more efficient and stable service to Multi-SFCs, quantitative QoE utility and stability models and cooperative Multi-SFCs path allocation are needed. Considering the limited resources, this paper designs a stable QoE-aware Multi-SFCs Cooperative Routing Mechanism (CRM) based on Deep Reinforcement Learning (DRL). Firstly, we model the problem of Multi-SFCs cooperative routing aiming at optimizing diverse QoEs of utility, stability and delay, and formulate it as a multi-objective optimization problem. Moreover, the QoE stable queueing model based on node and link availability is formulated by Lyapunov drift, which can potentially mitigate the QoE fluctuations. Then we associate triple Dueling Deep Q-Networks (DDQNs) and propose a cooperative computing multi-task DRL to obtain the optimal path allocation policy. Experiments are conducted to verify the effectiveness and the results show that our method outperforms its peers on QoE, delay, throughput and stability.

The liveness of Petri nets (PNs) means that every event can occur in any state, establishing a close relationship with the deadlock-free property of existing systems. Due to the problem of state space explosion and the infinite state space of unbounded PNs (UPNs), the time complexity and space complexity of the liveness decision are difficult to give accurate measures; at least, they are both NP-hard. Except for some particular subclasses of UPNs, there has not been an accurate method to decide the liveness of generalized UPNs. Thus, a liveness decision method from a machine learning perspective is proposed to predict probability values about UPNs’ liveness within a finite time. The method aims to learn the feature information on UPNs by deep neural networks and establish the mapping relationship between the UPNs and the liveness. First, the concept of approximating infinite space with finite states is applied to generate reachability graphs at different moments, following the firing rules of a UPN. Then, the graph convolutional network (GCN)-based reachability graph feature representation module and the gated recurrent unit (GRU)-based UPN feature representation module are designed to map the reachability graphs at different moments into the low-dimensional feature space. And the feature vector that can characterize the UPN’s liveness is obtained to decide the liveness probabilistically. Finally, three datasets, including 50000 samples, are constructed. Based on these datasets and some case studies, the experimental results validate the method’s ability to make liveness decisions for UPNs, demonstrating its strong performance in terms of effectiveness and generalization.

With the popularity of credit cards worldwide, timely and accurate fraud detection has become critically important to ensure the safety of their user accounts. Existing models generally utilize original features or manually aggregated features as their transactional representations, while they fail to reveal the hidden fraudulent behaviors. In this work, we propose a novel model to extract transactional behaviors of users and learn new transactional behavioral representations for credit card fraud detection. Considering the characteristics of transactional behaviors, two time-aware gates are designed in a recurrent neural net unit to learn long- and short-term transactional habits of users, respectively, and to capture behavioral changes of users caused by different time intervals between their consecutive transactions. A time-aware-attention module is proposed and employed to extract the behavioral information from their consecutive historical transactions with time intervals, which enables the proposed model to capture behavioral motive and periodicity inside their historical transactional behaviors. An interaction module is designed to learn more comprehensive and rational representations. To prove the effectiveness of the learned transactional behavioral representations, experiments are conducted on a large real-world transaction dataset and a public one. The results show that the learned representation can well distinguish fraudulent behaviors from legitimate ones, and the proposed method can improve the performance of credit card fraud detection in terms of various evaluation criteria over the state-of-the-art methods.

Graph neural networks (GNNs) have achieved effective performance in many graph-related tasks involving recommendation systems, social networks, and bioinformatics. Recent studies have proposed several graph pooling operators to obtain graph-level representations from node representations. Nevertheless, they usually adopt a single strategy to evaluate the importance of nodes, which may generate node rankings with weak robustness. Also, they cannot capture the different substructures of a graph since they shrink the graph layer by layer. To solve the above problems, this article proposes a Multistructure graph classification method with Attention mechanism and Convolutional neural network (CNN), called MAC. In particular, we propose a novel pooling operator, which adopts multiple strategies to evaluate the importance of nodes and updates node representations through an attention mechanism. Also, we design a hierarchical architecture for MAC to capture multiple different substructures of a graph. To further reduce the loss of graph information, we utilize 2-D CNN to generate a graph-level representation. Comparative experiments are performed on public benchmark datasets deriving from social systems, and the experimental results indicate that our method outperforms a range of state-of-the-art graph classification methods.

Extreme Multi-label text Classification ( XMC) is a task of recalling the most relevant labels for each given text from an extremely large-scale label set. It is emphasized that XMC is a more complex classification task because there are two main problems: large space of labels and the labels in XMC tasks tend to be correlated. Existing methods attempt to model label correlations by viewing the XMC task as a sequence generation problem however they still suffer from (1) using the slow serial decoding strategy where labels are predicted one-by-one. (2) needing to compare a mass of label ordering strategies in the decoding stage to achieve satisfied accuracy. In this work, we propose LAbel Mask-Predicted Transformer (LAMPT) to address the both issues, which is a novel non-autoregressive generation model that (1) enriches the input raw text representation with the additional label features by fully exploiting the label dependencies, (2) allows for efficient parallel decoding thanks to its non-autoregressive decoding formulation and mask-prediced training strategy. Experimental results demonstrate that single model performance is substantially enhanced by LAMPT. On a Wiki dataset with thirty-one thousand labels, LAMPT-XLNet accuracy has gained 1.6% relative improvement on P@3 over the LightXML-XLNet. Also, the P@1 of ensemble LAMPT is 90.00%, a significant i mprovement over the state-of-the-art ensemble LightXML (transformer-based) and AttentionXML (LSTM-based), which achieve 89.45% and 87.47%, respectively.

Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT).One of the prominent VAE-based NAT frameworks, LaNMT, achieves great improvements to vanilla models, but still suffers from two main issues which lower down the translation quality: (1) mismatch between training and inference circumstances and (2) inadequacy of latent representations.In this work, we target on addressing these issues by proposing posterior consistency regularization.Specifically, we first perform stochastic data augmentation on the input samples to better adapt the model for inference circumstance, and then conduct consistency training on posterior latent variables to construct a more robust latent representations without any expansion on latent size.Experiments on En<->De and En<->Ro benchmarks confirm the effectiveness of our methods with about 1.5/0.7 and 0.8/0.3BLEU points improvement to the baseline model with about 12.6× faster than autoregressive Transformer.

Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks. Compared with graph data, net data can simulate the dynamic behavioral features of systems and are more suitable for representing real-world problems. However, the problem of large-scale data analysis has been puzzling the PN field for decades, and thus, limited its universal applicability. In this article, a framework of net learning (NL) is proposed. NL contains the advantages of PN modeling and analysis with the advantages of graph learning computation. Then, two kinds of NL algorithms are designed for performance analysis of stochastic PNs, and more specifically, the hidden feature information of the PN is obtained by mapping net information to the low-dimensional feature space. Experiments demonstrate the effectiveness of the proposed model and algorithms on the performance analysis of stochastic PNs.

The existing Stochastic Petri Net (SPN) analysis methods are based on a series of steps, including generating the reachable graph, solving the state equation, etc. Unfortunately, these methods cannot perform performance analysis when the state equation has no unique solution. The end-to-end deep learning methods can build a mapping relationship from SPN to performance indicators, which avoids solving the state equation. However, there is a lack of benchmark datasets for SPN learning and training. This paper proposes an automatic generation method of SPN datasets, including SPN random generation, data labeling, data enhancement, and filtering. To relieve the local aggregation problem of random-based organization, a grid-based data organization method is proposed to ensure the diversity of the datasets. In the experimental section, the generated datasets are trained and tested on three types of neural networks. The results show that the generated benchmark datasets are useful, and the increase of dataset size will significantly improve the learning performance.

Anomaly detection relies on individuals' behavior profiling and works by detecting any deviation from the norm. When used for online banking fraud detection, however, it mainly suffers from three disadvantages. First, for an individual, the historical behavior data are often too limited to profile his/her behavior pattern. Second, due to the heterogeneous nature of transaction data, there lacks a uniform treatment of different kinds of attribute values, which becomes a potential barrier for model development and further usage. Third, the transaction data are highly skewed, and it becomes a challenge to utilize the label information effectively. The three disadvantages result in both poor generalization and high false positive rate of anomaly detection, and we propose a ranking metric embedding based multi-contextual behavior profiling (ReMEMBeR) model to battle them effectively. We solve the original fraud detection problem as a pseudo-recommender system problem, where an individual is treated as a pseudo-user, his/her behavior as a pseudo-item, and the label as the corresponding pseudo-rating. With the idea of collaborative filtering, for an individual, information from other similar individuals can be used to establish his/her behavior profile. In order to obtain a uniform treatment of heterogeneous attributes, we turn to an embedding based method to learn both attribute embedding and individuals' behavior profiles within a common latent space simultaneously. To utilize the label information better, our model is designed to fit pseudo-users' correct preference ranking for pseudo-items. By doing so, it explicitly learns to tell the fraudulent from the legitimate. Last but not least, we propose to identify and distinguish individuals under different contexts and further generalize the behavior profiling model to be a multi-contextual one. The proposed model can, thus, integrate the multi-contextual behavior patterns and allow transactions to be examined under the different contexts. Extensive experiments on a real-world online banking transaction dataset demonstrate that our model not only outperforms benchmarks on all metrics but also can be combined with them to achieve even better performance.

Location-Based Services (LBS), which answer users’ location-dependent queries to Points of Interest (POI), have become popular along with mobile devices’ widespread use. While benefiting from convenience, users may suffer privacy leak risk, due to publishing sensitive information, i.e., locations, to servers. Previous studies have proposed a number of methods for protecting LBS. Through providing provable privacy protection, Private Information Retrieval (PIR) becomes well-known for its high effectiveness in protecting privacy. However, the PIR-protected LBS must cautiously handle the consequent transmission/computation cost and service error brought by adopting PIR, as both of them impact user experience. Then an important question arises: Can the user experience of PIR-protected LBS be greatly improved? We answer it by presenting a novel interface from PIR to LBS, gaining benefits from road networks. The proposed interface is comprised of two road partition methods fully considering POI’s distribution along roads, and two response-calculating methods to satisfy users’ requirements on driving distance. Since Vehicular Location Based Services contain adequate road network knowledge, we accomplish our work on them. The experimental results on a real dataset validate that our interface can improve PIR-protected LBS greatly in user experience while protecting privacy.

The correctness of workflow systems is dependent on not only their control-flows but also their data-flows.WFD-nets(Workflow net with data) can model both control-flow and data-flow of workflow systems, and are used to analyze/verify their correctness. The existing methods of analyzing/verifying WFD-nets are based on their reachability graphs. However, the reachability-graph-based methods have two flaws: state space explosion and pseudo states, since the guard functions are not handled perfectly. Note that every transition of a WFD-net is labeled by a guard function, which is a Boolean expressionw.r.t.data and can determine the progress of control-flow. In order to solve these problems, we propose aguard-driven reachability graphthat can both alleviate the state space explosion problem and avoid pseudo states. We describe the related algorithms and develop a tool. A group of experiments illustrate the advantage and effectiveness of our approach, and an example of property loan shows its usefulness.

AdaBoost is a boosting-based machine learning method under the assumption that the data in training and testing sets have the same distribution and input feature space. It increases the weights of those instances that are wrongly classified in a training process. However, the assumption does not hold in many real-world data sets. Therefore, AdaBoost is extended to transfer AdaBoost (TrAdaBoost) that can effectively transfer knowledge from one domain to another. TrAdaBoost decreases the weights of those instances that belong to the source domain but are wrongly classified in a training process. It is more suitable for the case that data are of different distribution. Can it be improved for some special transfer scenarios, e.g., the data distribution changes slightly over time? We find that the distribution of credit card transaction data can change with the changes in the transaction behaviors of users, but the changes are slow most of the time. These changes are yet important for detecting transaction fraud since they result in a so-called concept drift problem. In order to make TrAdaBoost more suitable for the abovementioned case, we, thus, propose an improved TrAdaBoost (ITrAdaBoost) in this article. It updates (i.e., increases or decreases) the weight of a wrongly classified instance in a source domain according to the distribution distance from the instance to a target domain, and the calculation of distance is based on the theory of reproducing kernel Hilbert space. We do a series of experiments over five data sets, and the results illustrate the advantage of ITrAdaBoost.

Convolutional neural networks (CNNs) have demonstrated great competence in feature representation, and then, achieved a good performance to many classification tasks. Cross-entropy loss, together with softmax, is arguably one of the most commonly used loss functions in CNNs (that is generally called softmax loss). However, the softmax loss can result in a weakly discriminative feature representation since it focuses on the interclass separability rather than the intraclass compactness. This article proposes a pairwise Gaussian loss (PGL) for CNNs that can well address the intraclass compactness through significantly penalizing those similar sample pairs with a relatively large distance. At the same time, PGL can still ensure a good interclass separability. Experiments show that PGL can guarantee that CNNs obtain a better classification performance compared to not only the softmax loss but also others often used in CNNs. Our experiments also show that PGL has a stable convergence for the stochastic gradient descent optimization method in CNNs and a good generalization ability for different structures of CNNs.

Workflow nets and their extensions with data flows, e.g., workflow nets with data and colored workflow nets, can well model the business processes of workflow systems, and their soundness guarantees that systems have neither deadlock nor livelock. However, some design requirements related to the changes of data values within multiple business cases are not reflected by these models, and thus, some errors of the modeled systems cannot be detected by them. In this article, we propose workflow nets with tables (WFT-nets) to model workflow systems that are closely related to the database (i.e., different users, records, and attributes). We define the firing rule of WFT-nets via data refinements in order to avoid infinite states caused by unbounded data. We use first-order linear temporal logic to represent the design requirements of records and attributes' values and then define the soundness of WFT-nets. We construct an algorithm to verify soundness. A tool is developed, and a group of experiments are done to illustrate the effectiveness of our methods.

The class imbalance problem exists in many real-world applications such as fraud detection, medical diagnosis and spam filtering, and seriously influences the performance of learning algorithms. Randomly undersampling is a famous method to solve the problem. However, it cannot well extract the samples nearby the cross-edge of majority and minority classes due to its randomness, while these samples are very important for a classifier since they influence the classification performance. In this paper, we propose a novel Gaussian Mixture Undersampling (GMUS for short). GMUS mainly contains three steps. Firstly, a Gaussian Mixture Model (GMM) is applied to fit the majority samples. Secondly, considering the probability density function (PDF) of predicted minority samples on the well-fitted GMM, the maximum of PDF is selected as the cross-edge of two classes. Finally, we undersample the majority samples near the cross-edge. We do experiments on 16 public datasets and the results demonstrate that GMUS can sample more informative instances and thus improve the performance of classifiers compared with the state-of-the-art undersampling methods. We also apply GMUS to the credit card fraud detection and obtain a good performance.

Location recommendation method is an important application in a location-based social network. At present, it is a trend to integrate different recommendation methods since they have their own advantages in capturing different preferences of users and an integrated method can generally provide a better performance than every individual. However, the existing integration policies do not learn user preferences in their integration processes so that they cannot make full use of the advantage of each method. Therefore, we propose a novel integration method: learning-to-rank-based integration. In our method, a confidence coefficient is applied for each user in the integration process, and these coefficients can well optimize recommendation performance. A learning-to-rank-based algorithm is designed to train the confidence coefficients. A group of experiments are done on a real large-scale check-in data set, and the results demonstrate that our method outperforms the state-of-the-art ones.

Credit card is now popular in daily life. Meanwhile, credit card fraud events occur more frequently, which result in massive financial losses. There are a number of fraud detection methods, but they do not deeply mine features of customer's transaction behavior so that their detection effectiveness is not too desirable. This paper focuses on two aspects of feature mining. Firstly, the features of credit card transactions are expanded in time dimension to characterize the distinct payment habits of legal users and criminals. Secondly, Capsule Network (CapsNet) is adopted to further dig some deep features on the base of the expanded features, and then a fraud detection model is trained to identify if a transaction is legal or fraud. Through experiments on a real transaction dataset, we demonstrate that the time dimension extension can improve the performance of fraud detection, and then CapsNet is further illustrated to be more advantageous in fraud detection compared with other models.

Petri nets are used to detect the errors of data inconsistency in concurrent systems. The detection methods usually apply a breadth-first or depth-first search to the reachability graphs of Petri nets. However, they spend too much space and time due to the blind search strategy. In this paper, we propose a heuristic method that can save lots of space and time. First we define structural data inconsistency of Petri nets which is more likely to cause the errors of data inconsistency. Then, in order to detect the errors as fast as possible, a heuristic function is proposed to guide the construction of a reachability graph. Furthermore, we propose a detection algorithm and develop a tool. Our experiments illustrate the advantage and effectiveness of our method.

With the popularization of online shopping, transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of their BPs. Markov chain models are popular to represent BPs of users, which is effective for those users whose transaction behaviors are stable relatively. However, with the development and popularization of online shopping, it is more convenient for users to consume via the Internet, which diversifies the transaction behaviors of users. Therefore, Markov chain models are unsuitable for the representation of these behaviors. In this paper, we propose logical graph of BP (LGBP) which is a total order-based model to represent the logical relation of attributes of transaction records. Based on LGBP and users' transaction records, we can compute a path-based transition probability from an attribute to another one. At the same time, we define an information entropy-based diversity coefficient in order to characterize the diversity of transaction behaviors of a user. In addition, we define a state transition probability matrix to capture temporal features of transactions of a user. Consequently, we can construct a BP for each user and then use it to verify if an incoming transaction is a fraud or not. Our experiments over a real data set illustrate that our method is better than three state-of-the-art oneoness.

E-commerce business process nets (EBPNs) are a novel formal model for describing and validating e-commerce systems including interactive parties such as shopper, merchant, and third-party payment platform. Data errors and nondeterminacy of the data states during the trading process can be depicted with the help of EBPNs. However, the problem about how to analyze EBPNs remains largely open. To analyze their data-liveness, data-boundedness, and reachability, this paper presents two analysis methods. For EBPNs, reachability analysis is proposed based on a 3-D incidence matrix method. Additionally, reduction methods are proposed for a special EBPN. Finally, the validity and reliability of the proposed methods are illustrated via the examples of e-commerce systems.

Fog computing, also called “clouds at the edge,” is an emerging paradigm allocating services near the devices to improve the quality of service (QoS). The explosive prevalence of Internet of Things, big data, and fog computing in the context of cloud computing makes it extremely challenging to explore both cloud and fog resource scheduling strategy so as to improve the efficiency of resources utilization, satisfy the users' QoS requirements, and maximize the profit of both resource providers and users. This paper proposes a resource allocation strategy for fog computing based on priced timed Petri nets (PTPNs), by which the user can choose the satisfying resources autonomously from a group of preallocated resources. Our strategy comprehensively considers the price cost and time cost to complete a task, as well as the credibility evaluation of both users and fog resources. We construct the PTPN models of tasks in fog computing in accordance with the features of fog resources. Algorithm that predicts task completion time is presented. Method of computing the credibility evaluation of fog resource is also proposed. In particular, we give the dynamic allocation algorithm of fog resources. Simulation results demonstrate that our proposed algorithms can achieve a higher efficiency than static allocation strategies in terms of task completion time and price.

The errors of data inconsistency occur in a concurrent system when some concurrent operations are conducted improperly. The model-checking technique is widely used to detect them based on the state transition graph. However, the state space explosion problem is the biggest obstacle for this technique, since the state transition graph is based on the interleaving semantics that can result in a rapid increase of the graph scale. In addition, data inconsistency is closely related with concurrent operations, but the state transition graph hardly characterizes concurrency due to its interleaving semantics. The unfolding technique of Petri nets can both alleviate the state explosion and characterize concurrency because it is based on the concurrent semantics. In this paper, we define Petri net with data to model concurrent systems with three kinds of data operations: read , write, and delete, and then formalize data inconsistency. We propose an unfolding method to produce a finite complete prefix (FCP) for each PD-net. Then, a matrix that represents all concurrency relations of transitions is constructed in view of FCP. Furthermore, the error of data inconsistency can be detected via this matrix. The related algorithms and the developed tool are introduced, and experiments illustrate their effectiveness and advantages. An example of industrial information system shows the usefulness of our study.

Effectiveness is the most important factor considered in the ranking models yielded by algorithms of learning to rank (LTR). Most of the related ranking models only focus on improving the average effectiveness but ignore robustness. When a ranking model ignores robustness, the effectiveness for many queries is possibly very poor although the average effectiveness for all queries is relatively high. Therefore, Wang et al. first consider robustness in their ranking models. However, the robustness formula defined by Wang et al. cannot characterize those queries whose effectiveness are hurt seriously in comparison with the baseline model. In order to overcome this shortcoming, we propose a novel formula of characterizing robustness based on portfolio theory, and construct a multiobjective optimization model of the robust LTR in which the formula is used. Based on this model, we propose an approach of risk-sensitive and robust LTR, named as R2Rank, which is based on the framework of archived multiobjective simulated annealing algorithm and the idea of preference ranking organization method for enrichment evaluation. The experimental results show that the ranking models produced by our proposed R2Rank approach are better in both effectiveness and robustness than those produced by three state-of-the-art LTR approaches.

It is a valuable study for Location-based Social Network (LBSN) make a more accurate Points-of-Interest (POI) recommendation since that can improve users' experiences. There have been many methods of POIs recommendation that consider context, personal preference pattern, and/or matrix factorization. However, the continuous contexts have not been thoroughly considered in these methods. This paper first proposes a locations splitting method which can handle both continuous and discrete contexts. Moreover, we present a Context-aware Probabilistic Matrix Factorization method (CPMF) that factorizes a frequency matrix of contexts and locations to obtain the user-location checkin probabilities. We design a Personal Preference Confidence (PPC) to extract a set of reliable POIs with confidence values for every user. Finally, we propose a hybrid recommender which fuses CPMF with PPC to recommend top-n POIs. Experiments on a large-scale real-world checkins dataset demonstrate that our recommendation method obtains a well performance and effect.

The general Petri nets are not suitable to model the data operations of concurrent read and coverable write. Therefore, Petri net with data operations (PN-DO) is defined, which extends contextual nets with write arcs and some other components. Its execution semantics are defined, and a new method is proposed to construct its reachability graph that is of a smaller scale than traditional reachability graph. Based on this kind of reachability graph, we propose a method to check the errors of inconsistent data and missing data. Meanwhile, case studies are given to illustrate the effectiveness of our methods.

In SDN (Software-Defined Networks), applications are granted the ability to control and manage networks, such as routing, access control, and load balance, which is considered one of the most significant characters of SDN. However, these concurrent applications are lack of interactions when they operate on overlapping portions of the traffic and introduce a critical challenge of potentially conflicting flow rules (including match fields and actions). When their actions are dropping, forwarding, or diverting to the controller, these kinds of flow rule conflicts are inherent and arduous, and there exist no appropriate compositional methods of resolving them. Under the circumstances, the paper proposes a general model of abstract network states where different applications can own their different network views. When conflicting rules are detected, the model provides new network states for applications by composing original network states and conflicting rules. Therefore, applications do not need to handle underlying conflicting rules and just achieve the latest network states to install flow rules correctly.

Existing researches on user mouse authentication mostly focus on fixed tracks, which leads to the lack of practicability. This paper is not restricted to fixed tracks and models the non-fixed mouse behavior pattern. By simulating scenarios of dynamic soft keyboard, mouse behavior data in relatively free tracks is collected. New mouse characteristics are proposed based on the behavioral trait. Mouse behavior feature vector is obtained by using a combination of Cumulative Distribution Function (CDF) and Plus-L Minus-R Selection (LRS). The Support Vector Machine (SVM) algorithm is adopted to build patterns, and the majority voting method is used for user authentication. Experimental results demonstrate the efficacy of the proposed method with a classification accuracy of 96.3%, which achieves a FAR of 1.98%, and a FRR of 2.10%. The proposed method can be adopted in non-fixed traces, which can be used as an assistant method for password authentication mechanism in real-world dynamic soft keyboard scenarios.

Currently, existing continuous identity verification methods mostly need to analyze a lot of keystroke data to ensure the authentication credibility. To achieve certification results with less data, in the paper, a new continuous verification method is proposed. This method, based on free-text keystroke dynamics, excavates the nearest character sequences of the users from their typing patterns, then builds Gaussian model based on the users nearest character sequences, and at last grades the attempts to verify identity of the users based on Gaussian probability density function. Experimental results demonstrate that efficacy of the proposed method with accuracy of 90.5%, which achieve a false-alarm-rate of 5.3% under thirty characters. In the field of continuous identity verification, our method can be applied to reduce the verification cycle and ensure the reliability of the verification.

This paper focuses on account stolen case of smart phone users. In order to enhance the credibility of user authentication, an authentication method based on virtual keystroke dynamics behavior of touch screen is proposed in this paper. The proposed method extracts time dependent characteristics and pressure related characteristics of users' virtual keystroke dynamics behavior, builds combined authentication model by RBF networks, checks whether the virtual keystroke dynamics behavior of current user matches that of expected user to authenticate user's identity. The experiment shows that the proposed method has the expected result.

Recently, online shopping integrating third-party payment platforms (TPPs) introduces new security challenges due to complex interactions between Application Programming Interfaces (APIs) of Merchants and TPPs. Malicious clients may exploit security vulnerabilities by calling APIs in an arbitrary order or playing various roles. To deal with the security issue in the early stages of system development, this paper presents a formal method for modeling and verification of online shopping business processes with malicious behavior patterns considered based on Petri nets. We propose a formal model called E-commerce Business Process Net to model a normal online shopping business process that represent intended functions, and malicious behavior patterns representing a potential attack that violates the security goals at the requirement analysis phase. Then, we synthesize the normal business process and malicious behavior patterns by an incremental modeling method. According to the synthetic model, we analyze whether an online shopping business process is resistant to the known malicious behavior patterns. As a result, our approach can make the software design provably secured from the malicious attacks at process design time and, thus, reduces the difficulty and cost of modification for imperfect systems at the release phase. We demonstrate our approach through a case study.

The number of web services has grown drastically. Then how to manage them efficiently in a service repository is an important issue to address. Given a special field, there often exists an efficient data structure for a class of objects, e.g., the Google' Bigtable is very suitable for webpages' storage and management. Based on the theory of the equivalence relations and quotient sets, this work proposes a multilevel index model for large-scale service repositories, which can be used to reduce the execution time of service discovery and composition. Its novel use of keys as inspired by the key in relational database can effectively remove the redundancy of the commonly-used inverted index. Its four function-based operations are for the first time proposed to manage and maintain services in a repository. The experiments validate that the proposed model is more efficient than the existing structures, i.e., sequential and inverted index ones.

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods.

E-commerce and online shopping with a third-party payment platform have rapidly developed recently, and encountered many fault tolerance and security problems concerned by users. The causes of these problems include malicious behavior and imperfect business processes. The latter lead to the emergence of security vulnerabilities and loss of user funds which become more and more serious these years. We focus on the business process of e-commerce, and propose a formal model for constructing an e-commerce business process called an E-commerce Business Process Net. It integrates both data and control flows based on Petri nets. Rationality and transaction consistency are defined and validated to guarantee the transaction properties of an e-commerce business process. This paper offers a complete methodology for modeling and validating an e-commerce system with a third-party payment platform from the view point of a business process. Its use enables a designer to identify errors early in the design process and correct them before the deployment phase. In order to demonstrate the applicability and feasibility of the methodology, we have modeled and validated a real-world e-commerce business process and discovered the problems that cause the violation of transaction properties.

Owing to their rapid development, services are increasing rapidly in quantity. The consequence is that there are so many services that share the same or similar functions. Therefore, it is important to select a credible and optimal service. Reputation as one of the important parameters of services plays a significant role in the decision support for service selection. This paper proposes a novel two-phase method to calculate service reputation. The first phase uses a dynamic weight formula to calculate reputation such that it can reflect the latest tendency of a service. The second one uses an olfactory response formula to mitigate the negative effect of unfair ratings. Some experiments are conducted and the results validate the effectiveness of the proposed method.

With the rapid development of service-oriented computing (SOC) and service-oriented architecture (SOA), the number of services is rapidly increasing. How to organize and manage services effectively in repositories to improve the efficiency of service discovery and composition is important. This paper proposes three categorization rules to classify services for a large scale repository to form a relational taxonomy. The service retrieve scope can be drastically narrowed by this taxonomy. Therefore, the efficiency of service discovery and service composition can be greatly improved. We evaluate and compare the performance of the proposed method and other related ones via a publicly available test set, ICEBE05. The experimental results validate the effectiveness and high efficiency of the proposed one.