Semiconductor manufacturing widely employs cluster tools that comprise three critical components: a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). In operating such tools, LLM plays a pivotal role. Additionally, wafer fabrication in VM faces strict wafer residency time constraints, making the coordination among the modules particularly challenging. This paper addresses the cyclic scheduling problem of single-arm cluster tools with EFEM and wafer residency time constraints. It focuses on the tool efficiency in module cooperation and schedulability. We explore cooperative strategies for the robot operating within VM to access loadlocks, elucidating the influence of EFEM and LLM on VM. Based on these strategies, we establish necessary and sufficient conditions for the existence of a feasible periodic schedule, providing a foundational basis for the systematic analysis. For schedulable scenarios, we derive an efficient algorithm to identify the feasible and optimal schedule in terms of tool cycle time. The performance and efficiency of the proposed algorithm are validated through experimental verification. Note to Practitioners—In wafer fabs, single-arm cluster tools with EFEM are widely adopted. Studies that neglect the impact of EFEM on scheduling cluster tools are often inapplicable to real-world scenarios. Wafer residency time constraints are essential for maintaining wafer quality. As VM and EFEM operate in different pressure environments, the cooperation via LLM makes it challenging to meet residency time constraints. To address this problem, this paper analyzes the impact of cooperative strategies on the tool performance, and the influence of EFEM and LLM on VM as well. Under these strategies, we examine the schedulability conditions, helping engineers understand how processing parameters affect scheduling feasibility. If a feasible schedule exists, an efficient algorithm is derived to find an optimal schedule. Experimental results show the efficiency of the proposed algorithm, making it suitable for embedding into cluster tool controllers for efficient implementation. For cases that are not schedulable, with the results obtained, this work can provide guidance of how to redesign the process such that feasibility can be ensured.

In role-based collaboration (RBC) methodology, eliminating agent conflicts during the role assignment process is crucial for establishing a sustainable cooperative system. However, when agent resources are scarce, assignment strategies aimed at eliminating agent conflicts become infeasible. Consequently, there is a need to select the optimal assignment with a minimal number of agent conflicts, which is essentially a nonlinear bilevel optimization problem. To tackle this issue, we first design the group role assignment with minimized agent conflicts (GRAMAC) model to formalize this problem. It converts this problem into an extended integer linear programming (x-ILP) one and finds the optimal solution. Then, we prove that solving the GRAMAC model is an$\mathscr {NP} - \mathrm {complete}$task. Moreover, we identify the sufficient and necessary condition under which the GRAMAC model has the optimal conflict-free solution. Finally, extensive experiments demonstrate that, compared to existing strategies, our proposed method reduces the number of agent conflicts by an average of approximately 30% while ensuring the group performance of the collaborative system.

Quasi group role assignment (QGRA) presents a novel social computing model designed to address the burgeoning domain of self-service spatiotemporal crowdsourcing (SSC), specifically for tackling the photographing to make money problem (PMMP). Nevertheless, the application of QGRA in practical scenarios encounters a significant bottleneck. QGRA provides optimal assignment strategies under conditions where both the number of crowdsourced tasks and workers remain stable. However, real-world crowdsourcing applications may necessitate the phased integration of new tasks. With the rapid increase in the number of tasks, a set of residual tasks inevitably exists that are difficult to complete. To maximize the completion of crowdsourced tasks, workers may be assigned low-yield or even unprofitable tasks. Given the reluctance of crowdsourcing workers to be overstretched for these tasks, along with the inherent characteristics of self-service crowdsourcing tasks, this can lead to the failure of the assignment scheme. To tackle the identified challenges, this article proposes the QGRA with agent satisfaction (QGRAAS) method. Initially, it sheds light on a creative satisfaction filtering algorithm (SFA), which is engineered to perform optimal task assignments while actively optimizing the profitability of crowdsourcing workers. This approach ensures the satisfaction of workers, thereby fostering their loyalty to the platform. Concurrently, in response to the phased changes in the crowdsourcing environment, this article incorporates the concept of bonus incentives. This aids decision-makers in achieving a tradeoff between the operational costs and task completion rates. The robustness and practicality of the proposed solutions are confirmed through simulation experiments.

With the popularity of Non-Fungible Tokens (NFTs), the high value of NFTs makes them a target for phishing scammers, which harms the security and reliability of the Web3 NFT ecosystem. Despite the significance of this issue, there is a lack of systematic research in the area of emerging NFT phishing scams. To address this gap, we are the first to conduct a case retrospective analysis and empirical measurement study of real-world historical NFT phishing scams on Ethereum. We collect and publicly release the first NFT phishing dataset which includes 1,625 NFT phishing accounts and transaction records as of August 2023. We further categorize the existing scams into four phishing patterns and investigate their distinguishable behaviors. Then, we reveal the modus operandi preferences and economic impacts to characterize NFT phishing scams. We find that NFT phishers stole 67,188 NFTs, with a total direct selling profit of${\$}$20.92 million. We also observe that scammers favor certain categories and collections of NFTs, coupled with signs of gang theft. Furthermore, we design a variety of account features for the classification task of NFT phishers based on empirical conclusions. Experimental results on real-world NFT transaction data demonstrate the effectiveness of these features in detecting NFT phishing accounts, and outperform traditional phishing detection methods with 41% average Precision and 44% average Recall.

In semiconductor manufacturing, cluster tools tend to integrate a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). While scheduling techniques exist for cluster tools without EFEM, the collaboration among these modules introduces additional challenges for tools with EFEM. In such tools, LLM acting as a shared module plays a crucial role in operating a cluster tool. Moreover, modern fabs have adopted the practice of chamber cleaning after processing each wafer to eliminate chemical residue that may remain within chambers. This article addresses a cyclic scheduling problem of a single-arm cluster tool with EFEM, while considering chamber cleaning requirements. We propose a conflict-free loadlock (LL) state transformation sequence of One-in and One-out LLs to describe the transformations resulted from LL operations. We then present cooperative strategies for robots to access LLM during the state transformation. Based on these strategies, we derive closed-form algorithms to find feasible and optimal schedules in terms of tool cycle time, allowing for an analysis of the best-performing strategy combinations. The effectiveness of the proposed algorithm is illustrated by experimental results.

As one of the most active blockchain platforms at present, Ethereum attracts a great deal of interest, including that of fraudsters. They exploit the anonymity of Ethereum accounts to perpetrate varieties of scams, the most common of which is phishing frauds. However, existing phishing detection work ignores the heterogeneity of Ethereum transaction edges. In fact, the activities on Ethereum include external transactions, internal transactions, and token transactions. Therefore, this paper proposes an Ethereum account phishing fraud detection method named HTSGCN. Based on heterogeneous transaction subnets, our method makes full use of the type and direction information contained in transactions. First, we collect Ethereum transaction data and construct a k-order heterogeneous subnet for each account. To aggregate the neighbor feature, we design a message propagation mechanism based on graph convolution network. Finally, we classify node representation vectors containing neighborhood and its own characteristics. Experimental results show that HTSGCN has a better effect on detecting phishing accounts than previous work which is based on homogeneous networks.

In the team allocation problem (TAP), eliminating team disparities aims at keeping an equilibrium of the resource or ability among teams for equality. For this concern, existing literature merely utilized the golden mean principle to eliminate team disparities from a static perspective. Few of them reasonably investigate the pros and cons of this principle from a computational perspective. Moreover, maintaining equilibrium is a dynamic process and requires dynamic adjustment, especially after considering team members’ self-efforts and adaptivity. With respect to the environments—classes, agents, roles, groups, and objects (E-CARGO) model and its role-based collaboration (RBC) methodology, this article formalizes and solves the TAP, i.e., revised group role assignment (GRA) problem, from both the individual and team’s perspective. Based on the revised GRA, this article provides novel insight into the effectiveness of dynamically maintaining equilibrium, which may help decision-makers be proactive in building more sustainable teams. Relevant large-scale simulation experiments are conducted in this article to verify the proposed method. This article reveals a social paradox: even though considering all about the team members’ self-efforts and adaptivity, equilibrium still seems inequitable. Conversely, pursuing equilibrium may bring the Matthew effect.

The World Bank estimates that the number of refugees worldwide will reach 140 million by 2050 due to global warming and local wars. Considering the rapid increase in the number of refugees, an efficient and feasible assignment method is required for refugee resettlement. This article formalizes the refugee resettlement issue using the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. A novel solution is designed for Refugee reSettling (RS) by extending the Group MultiRole Assignment (GMRA), which applies the agent stability evaluation method as a feedback mechanism while optimally resettling refugees. With this proposed solution, decision-makers can swiftly resettle refugees from multiple suffering countries while appropriately ensuring host countries’ benefit. Finally, large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution. The simulation results provide a solid decision-making reference for the leaders of the world.

Group role assignment with cooperation and conflict factors (GRACCFs) is a creative social computing method for team establishment. It can maximize the new team’s performance through role assignment considering potential cooperation or conflict factors among agents. However, this method has two bottlenecks in practical applications. First, in the scenario of establishing a new team from several existing teams, collecting the pertinent cooperation or conflict information encounters challenges. Second, GRACCF merely takes the CCFs as a part of the objective function for team performance, but this will underestimate the CCFs’ impacts on the sustainable development of the team. This article tackles these issues by extending GRACCF from a new viewpoint. It first designs a KD45 logic algorithm based on the KD45 logic system, which can discover the implicit cognitive CCFs through logical inferences with closure calculations. Then, it proposes an original team evaluation method that can help decision-makers determine the weights of team performance and CCFs’ impacts based on their demands. Large-scale simulation experiments indicate that the proposed solution is practicable and robust. The proposed method provides a solid decision-making reference for administrators when establishing a sustainable team.

Nowadays, cluster tools are extensively used for many wafer manufacturing processes, such as coating, lithograph, developing, etching, deposition, and testing. Traditional process modules in cluster tools can execute a single operation only. With the rapid development of equipment design, multifunctional process modules (MPMs) are equipped to serve for processing multiple operations together just like a single operation. With different wafer processing parameters, MPMs may be set for processing multiple operations together or processing just a single operation to form different schedules so as to maximize the productivity. Thus, it is highly desired to find an efficient scheduling method to quickly adapt to wafer processing parameter changes for productivity maximization by taking the advantages of MPMs. To tackle this issue, a deadlock-free Petri net (PN) model is developed to describe the behavior of a single-arm cluster tool. Based on the evolving mechanism of the PN model, two algorithms are developed to calculate the makespan for completing a given number of wafers. Then, an adaptive scheduling method is presented to set the functions of MPMs to minimize the makespan. Finally, experimental results show the efficiency and effectiveness of the proposed method.

Due to the widespread use of smart contracts, Ethereum has become the second-largest blockchain platform after Bitcoin. Many different types of Ethereum accounts (ICO, Mining, Gambling, etc.) also have quite active trading activities on Ethereum. Studying the transaction records of these specific Ethereum accounts is very important for understanding their particular transaction characteristics, and further labeling the pseudonymous accounts. However, traditional methods are generally based on static and global transaction networks to conduct research, ignoring useful information about dynamic changes. Our work chooses six kinds of important account labels, and builds ego networks for each kind of Ethereum account. We focus on the interaction between the target node and neighbor nodes with temporal analysis. Experiments show that there is a significant difference between various types of accounts in terms of several network features, helping us better understand their transaction patterns. To the best of our knowledge, this is the first work to analyze the dynamic characteristics of Ethereum labeled accounts from the perspective of transaction ego networks.

Self-service spatiotemporal crowdsourcing (SSC), a booming variant of spatiotemporal crowdsourcing (SC), emerges because of the vigorous development of the mobile Internet. Unlike the conventional SCs, the particularity of self-service in SSC may lead to unfinished tasks at the end of the entire assignment process, making a one-time assignment scheme ineffective. SSC is essentially an adaptive collaboration (AC) problem that requires a dynamic assignment strategy for a higher task completion rate. This article tackles this issue by establishing a quasi group role assignment (QGRA) based on a typical SSC scenario, that is, the photographing to make money problem (PMMP). First, it sheds light on a novel role awareness method, which can effectively divide tasks to accelerate the solution while, to some extent, raising the task completion rate. Second, it specifies an agent satisfaction evaluation (ASE) method to quantify the relationship between task completion rate and workers’ satisfaction. This method aims at considerably ameliorating task completion rate. Last, it extends QGRA with a new AC algorithm, which can achieve AC of the workers while accomplishing the crowdsourcing task. Moreover, utilizing the ASE method can help decision-makers balance the task completion rate and the workers’ satisfaction. Large-scale simulation experiments based on the real crowdsourced datasets exemplify the robustness and practicability of the proposed solutions. This article contributes a new version of the group role assignment (GRA) model, that is, quasi GRA (QGRA), a creative formalization to solve the AC problem.

When earthquakes occur, solar-powered unmanned aerial vehicles (SUAVs), deployed as communication relay points, can construct a signal relay network to assist the ground mobile communication vehicles in resuming communication. Considering the urgency of disaster relief, a practical, accurate, and robust modeling method for multiple SUAVs deployments is vital. For this concern, this article first formalizes the deployment problem of multiple solar-powered UAVs in communication recovery by extending Group MultiRole Assignment (GMRA) (UGRA). In the second step, the success in this assignment process depends on the choice of the agent evaluation method. The evaluation benchmark in UGRA is SUAV path planning in a complex environment with uncertain subpaths and accumulative attitude errors. In response to this issue, we propose two innovative algorithms: 1) dynamic curve path-planning algorithm (DCPPA) and 2) greedy curved straight path-planning algorithm (GCSPPA). Moreover, with the time requirement in mind, one sufficient condition and one necessary condition are established to help the DCPPA achieve fast convergence. With these two novel agent evaluation algorithms, UGRA can rapidly deploy multiple SUAVs to establish a collaborative relay network within an acceptable time. Finally, simulation experiments at different scales are carried out to demonstrate the accuracy and effectiveness of the proposed solution.

Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP) for fresh produce poses a challenge on traditional logistics since fresh produce is difficult to preserve. This article formalizes the LMAP for fresh produce via the group role assignment framework and proposes a role awareness method by using adaptive clustering in spatiotemporal crowdsourcing based on task granularity. The formalization of LMAP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method allows one to take the time and space factor into consideration, helps spatiotemporal crowdsourcing assign couriers for efficient delivering daily orders, and improves the quality of service in last-mile logistics. It is verified by simulation experiments. The experimental results demonstrate the practicability of the proposed solutions in this article.

With the increasing popularity of blockchain technology, it has also become a hotbed of various cybercrimes. As a traditional way of scam, the phishing scam has new means of scam in the blockchain scenario and swindles a lot of money from users. In order to create a safe environment for investors, an efficient method for phishing detection is urgently needed. In this paper, we propose a three steps framework to detect phishing scams on Ethereum by mining Ethereum transaction records. First, we obtain the labeled phishing accounts and corresponding transaction records from two authorized websites. According to the collected transaction records we build an Ethereum transaction network. Then, a network embedding method node2vec which can extract the latent features of accounts is used for subsequent phishing classification. Finally, to distinguish whether the account is a phishing account, we adopt the one-class support vector machine (SVM) to classify. The experimental result demonstrates that F-score of our phishing detection method can achieve 0.846, which verifies the validity of our model. To the best of our knowledge, this is the first work that investigates the phishing scams on Ethereum based on transaction records.

When an earthquake occurs, disaster relief is an urgent, complex and critical mission. High on the list is communication network recovery within the disaster area. Unmanned aerial vehicles (UAVs) are often used in this regard. Some of them are used as collective repeaters to provide the required network coverage. Their timely, efficient, and collaborative deployment to specific locations is a big challenge. To meet this challenge, this paper formalizes and solves the problem of UAV deployment for signal relays via group role assignment (GRA). The minimum spanning tree algorithm is used to model a rapidly deployed optimal relay network. It can help establish the minimum number of relay points necessary to ensure communication stability. In this scenario, UAVs (agents) adopt roles as communication relays. The task of distributing UAVs to relay points can be solved quickly via the assignment process of GRA, which can solve the x-ILP problem with the help of the PuLP package of Python. Results from thousands of experimental simulations indicate that our solutions are effective, robust and practical. The process can be used to establish an optimal, efficient, and collaborative relay network using UAVs. Their rapid deployment can be a significant contribution to earthquake disaster relief.

Task allocation is a critical phase of project management. Tree-type structures are frequently used constraints to obtain a pertinent task allocation. They can illustrate where one task may require numerous agents and when an agent can be assigned to different tasks (roles). The process of task allocation is made more complex when administrators need to satisfy sequential and fixed branch relationships between/among tasks (roles). This paper formalizes the tree-structured task allocation problem (TSTAP) with group multirole assignment (GMRA) and proves necessary conditions, the necessary and sufficient condition, as well as sufficient conditions, of TSTAP. The formalization makes it easy to find a solution with the IBM ILOG CPLEX optimization package (CPLEX). The necessary conditions improve the CPLEX solution by eliminating infeasible cases. The necessary and sufficient condition describes the solution space of TSTAP completely. Another exciting result is that the sufficient conditions can not only improve the CPLEX solution by describing a practical approximate solution space but also help decision-makers and human resource officers organize a team in order to successfully assign tasks. The proposed approach is verified by simulation experiments with respect to a real-world problem. The experimental results present the practicability of the proposed solutions in this paper. This paper was motivated by general cooperative projects whose tasks have tree-structured relationships. This can make the problem of successful multitask assignment extremely challenging. The traditional method of assignment such as the KM algorithm can no longer solve this problem. To solve the assignment problem with tree-structured relationships, an efficient many-to-many assignment with constraints is required. The proposed approach provides theoretical and technical foundations for efficient assignment of TSTA, which can not only provide a viable and effective assignment scheme for TSTA problems but also help human resource officers to formulate reasonable plans according to the relationships between/among tasks.

Many to many (M2M) recommendation is one of the fundamental and important problems in commerce. With respect to this idea, profits, clients and products are inseparable. The traditional Top-N method cannot process the M2M recommendation problem. Therefore, this paper deals with the M2M recommendation as a many to many assignment problem via the group multi-role assignment (GMRA). Based on the concise formalization of Role-based collaboration (RBC) and its E-CARGO model, a successful approach using an (Extended Integer Linear Programming) x-ILP planning method and an improved greedy Top-N algorithm is proposed. These methods are verified by simulation experiments. Their results indicate the practicability of both solutions. As a comparison, the greedy Top-N method is faster than the x-ILP planning method via the PuLP linear planning package of Python. On the hand, the latter outperforms the former in recommendation accuracy.