With the development of blockchain technology, crypto gambling has gained popularity due to its high level of anonymity. However, similar to traditional casinos, crypto casinos are controlled by a few internal Delegatees, making it impossible for them to achieve complete transparency and fairness. These delegatees are hidden among gamblers and are difficult to identify and distinguish in anonymous and large-scale blockchain transaction networks. This paper proposes an unsupervised dual-stage role identification method to adaptively identify key roles and hidden delegatees in label-sparse crypto casinos. Specifically, inspired by voting-style transaction patterns, we propose a novel voting influence metric for key node identification. This metric is based on one-dimensional structural entropy to capture global dissemination capability. Subsequently, we develop a multi-view graph neural network framework enhanced with two-dimensional global structural entropy minimization and self-supervised contrastive learning to improve the robustness and interpretability of hidden role partitioning. Experiments on real-world cases of the most mainstream blockchains-Ethereum, TRON, and Arbitrum-demonstrate that our proposed method effectively reveals distinct role compositions and collusion patterns, distinguishing between gamblers and delegatees. Our results achieve a higher match with identities confirmed by judicial authorities than existing methods, indicating the effectiveness and generalizability of our approach in enhancing security and regulation oversight.

Blockchain is a promising technology to drive business processes transparency and traceability, providing consensus and agreement between business partners to reduce information asymmetry and uncertainty along business processes. However, it does not assure data quality in blockchain-driven business processes, and poor data reliability can still occur. In this paper, the authors provide a framework of ontology modeling and verification for data reliability assessment in blockchain-driven business processes. The authors offer formal, process-oriented ontology definitions to specify data reliability assessment requirements, based on which smart contracts can be executed to implement the requirements. They provide a preliminary case study to demonstrate the use of the approach to improve the efficiency and effectiveness of data reliability assessment in consortium blockchains. The proposed ontology model can serve as a tool to standardize data formats and control data quality in blockchain-driven business scenarios.

The challenge of collecting large scale environmental data lies in reducing the cost of allocating data sensors while improving the data quality. Compared with traditional centralized data collecting methods, obtaining environmental data by distributed “crowds” can largely reduce the cost. However, designing such crowdsourcing systems requires an appropriate incentive mechanism to encourage providing accurate and rare environmental data. In this paper, we propose to utilize blockchain-based incentive mechanisms to address the problem. We design and evaluate a blockchain-based system for large scale environmental data acquisition, which consists of a sensor layer to collect distributed environmental data, a valuation layer to evaluate the quality of the data collected, a consensus layer to incentive and motivate high quality data collection, and a ledger layer to record the incentive transactions and the qualified environmental data. The incentive mechanism in the consensus layer is achieved with a Proof-of-Data-Value (PODV) protocol adapted from the Practical Byzantine Fault Tolerance (PBFT) [11] consensus algorithm. We carry out experiments to compare the PODV with contemporary blockchain census protocols including Proof-of-Work (POW) [16] and Proof-of-Stake (POS)[18]. The experimental results show that the proposed system outperforms in encouraging crowds to provide accurate and rare environmental data, and imply that the PODV are superior in terms of throughput and environmental friendliness.

Quality inspection, a widely adopted practice in supply chains, measures whether delivered products conform to prespecified quality requirements. Due to potential economic benefits, suppliers may deliberately manipulate products to falsify test results. However, unqualified products could cause severe problems in supply chains or even tragedies, such as China's tainted milk scandal. In this paper, we propose a beliefdesire-intention modeling approach to predict suppliers' behavior and provide inspection suggestions to buyers to overcome the problem. Assuming access to the production process, our approach can represent suppliers' knowledge of production and deception to mimic their reasoning processes and predict their deception intentions. This flexible approach can also adapt to environmental changes and deliver effective results. In this paper, we build a prototype system for supply chain quality inspections based on the proposed method. We conduct laboratory experiments to collect data for computational assessments of the performance of the prototype. It is shown that our proposed approach is more accurate than classic machine learning methods in detecting suppliers' deceptions.

Although current diffusion models have paid some attention to the diversity of diffusion actors, they usually focus on the diversity of the attributes of diffusion actors but ignore the diversity of the behavior patterns of different types of diffusion actors. As a result, it becomes difficult to control the rumor's diffusion speed and diffusion range. Then we take both the diversity of behavior patterns and attributes of diffusion actors into consideration. Based on the consideration, we propose an agent model that can fully embody the range of the two kinds of diversity of actors. In our model, one type of agent prefers to spread any rumor that they are interested in without regarding to its reliability, another type of agent prefers to only spread a reliable rumor. We call the first type of agent a “fool” and the second type a “sage” and call our model the sage-fool model (S-F model). What's more, we will propose some important attribute of each agent. Finally, we conduct two experiments, one normal experiment to compare our model with the classic laws of information diffusion and another explorative experiment to find some specific laws in our model.

Multiagent-based system has been widely used in hotel reservations. However, they mainly use one-way negotiation for decisions that agents only represent travelers selecting from a list of hotels, while hotel providers cannot communicate with travelers. To address such problems, this paper proposes a Multiagent-Based Two-Way Negotiation for Hotel Reservation (MAB-TNHR) with three kinds of agents. Traditional tenant-dominant reservation is converted into a form of tendering to implement this two-way negotiation, which means that Landlord Agents can actively respond to the selection by sending their tenders to suitable tenants and bargain on the price with the Tenant Agent to pursue their interests. In this paper, rules used in the application and a case study are presented to illustrate the implementation of MAB-TNHR. Verification shows that the MAB-TNHR can meet intricate and dynamic reservation demands automatically without the participations of tenants and landlords.