With the development of information technology, distributed systems have gained increased emphasis. Data consistency has become a critical concern in distributed systems because it directly affects system reliability and user experience. Data inconsistencies can arise from transaction consistency, real-time consistency, or human tampering with data. The main focus of this paper is to verify and detect the reliability of data consistency in distributed systems. To address this issue, we propose a time Petri nets model to characterize data changes within the system. To better observe system behavior, we propose a method for transforming time Petri nets into timed automata. This transformation method is used to verify and detect data consistency in distributed systems. Finally, to demonstrate the reliability of our method, we use a shopping system as an example and perform verification testing with the support of the UPPAAL tool, providing strong support for system design and implementation.

Transforming user content engagement into citizenship behaviours is significant for a social media platform’s prosperity. By integrating psychological ownership theory with the technology acceptance model, this study examined how and when in the context of social media, three different types of user content engagement (consumption, participation, and production) impact three different types of user citizenship behaviours (recommendation, helping, and feedback). Building upon survey data collected from 401 social media users in China, partial least squares structural equation modelling (PLS-SEM) was used to demonstrate that user content engagement is positively linked to user citizenship behaviours through the mediation of psychological ownership. The positive association between psychological ownership and recommendation behaviour is enhanced by perceived ease of use but weakened by perceived usefulness. Conversely, perceived ease of use weakens the positive link between psychological ownership and helping behaviour, while this relationship is enhanced by perceived usefulness. The positive effect of psychological ownership on feedback behaviour is weakened by perceived ease of use. This study contributes to the literature by revealing the transformative process from user content engagement to user citizenship behaviours on social media platforms.

Data plays a very important role in documents. Although much effort has been made in data processing, little research has been done on data consistency. Data inconsistency may lead to data analysis errors or inaccurate analysis results. In text, mismatches of data referred to by the same label and incompatibility in the implied semantic relationship between data can lead to data errors or data relationship errors. In order to check data consistency of the goal, we rewrite the adaptive automaton algorithm and extend tree automaton to allow the construction of deterministic tree automaton to express data relationships. In the financial report of the Postal Savings Bank of China in the past three years, we use the data in the report to model adaptive automaton models and bottom-up tree automaton, which include financial data, quarterly financial data, and data from the income statement and balance sheet, and used consistency detection algorithm evaluation. Experimental results show that the automated method simplifies the verification process, provides accurate and efficient results, and ensures data consistency and reliability in the text.

With numerous nodes on online heterogeneous networks, how to reach and extract target nodes of our specific interests is a pressing problem. In this paper, we propose a novel heterogeneous network crawler,MCrawl. It addresses the problem via iterative online heterogeneous network crawling by navigating its available APIs, starting from a set of target nodes, i.e., seed nodes. We are facing two challenges towards addressing the problem. First, to navigate within a vast network, how do we start from a small set of target nodes? In other words, which nodes in the “current frontier” and which direction shall we expand, to reach promising target nodes quickly? We propose motif-based crawling to exploit the complex structures and rich semantics of heterogeneous networks. Second, in many scenarios, we do not have a classifier to assess the quality of the harvested nodes and thus the motifs to expand. We develop a probabilistic inference framework to estimate the yield and harvest rates of motifs, achieving principled bootstrapping for crawling. Our experiment on real networks of MCrawl achieves significant margins over baselines.

This paper proposes a novel algorithm to discover hidden individuals in a social network. The problem is increasingly important for social scientists as the populations (e.g., individuals with mental illness) that they study converse online. Since these populations do not use the category (e.g., mental illness) to self-describe, directly querying with text is non-trivial. To by-pass the limitations of network and query re-writing frameworks, we focus on identifying hidden populations through attributed search. We propose a hierarchical Multi-Arm Bandit (DT-TMP) sampler that uses a decision tree coupled with reinforcement learning to query the combinatorial attributed search space by exploring and expanding along high yielding decision-tree branches. A comprehensive set of experiments over a suite of twelve sampling tasks on three online web platforms, and three offline entity datasets reveals that DT-TMP outperforms all baseline samplers by upto a margin of 54% on Twitter and 48% on RateMDs. An extensive ablation study confirms DT-TMP's superior performance under different sampling scenarios.

Recently, network traffic classification has attracted a great deal of attention among researchers. In this paper, we proposed a traffic classification approach based on characteristics of subflows and ensemble learning. Aiming at neutralization of unstable network environment as well as taking advantage of ensemble learning, we divided the traffic flows into different subflows in order to reduce the affection of time. Moreover, we develop truncation method on flows for real-time processing and an aggregation machine learning method based on accuracy of each classifier to different applications. Finally, the experimental results based on actual traffic traces collected from the campus network of Xian Jiaotong University verify the effectiveness of our methods.

Behavioral attention to stock markets in open source web search has attracted remarkable effort for financial analytics and applications in recent years. Differently from existing explorations at the overall market level, this paper investigates associative patterns that link search volume changes and stock price movements at individual stock level, so as to be able to support the design of flexible trading strategies. In doing so, a method (namely EATAPSP) is introduced as an extension to temporal associative pattern mining for time-series data, which can effectively discover the association between search volumes and stock prices in a temporal logic manner with the “after” predicate. Furthermore, the real world data on all China's A-share stocks from Shanghai Stock Exchange is used to test the method, revealing the effectiveness of the method. Moreover, with the discovered patterns, a new stock trading strategy (i.e., EATAPSP-Trading) is designed, which obtains superior cumulative returns and significantly outperforms other strategies.

Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve (ROC) analysis was used. Application of fuzzy logic to the serum values of CRP and albumin increased predictive performance for 1-year overall survival (AUC=0.773) compared with that of a single marker (AUC=0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow Prognostic Score (GPS) (AUC=0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.

We characterize the time-dependent behavior of a typical queuing system that arise in ATM networks under the presence of overloads. The transient queue length distribution and transient cell loss probability are obtained numerically and transient characteristics such as maximum overshoot and relaxation time are used to quantify the effects of congestion periods. A new measure, expected excess loss in overload (EELO) is defined to quantify the effects of overload when compared with the system behavior in the steady-state regime. The basic modeling technology that we use is an extended form of stochastic Petri nets and a software tool called the stochastic Petri net package (SPNP).