In the indivisible public goods allocation problem (IPGAP), it is always impossible to obtain a perfect allocation plan which can satisfy every group. To eliminate collective bias, decision-makers often lean toward applying the fifty-fifty compromise principle, which means compromising absolutely fairly among all groups. For this concern, a few relevant research investigates the effectiveness of utilizing this principle from a computational perspective due to the lack of quantitative analysis tools. Notably, the environment-classes, agents, roles, groups, and objects (E-CARGO) model, a mature and proven effective tool, demonstrates outstanding performance in the study of such social issues. With respect to the E-CARGO model and its submodel group role assignment (GRA), this article formalizes and explores the IPGAP. Based on the group role assignment with constraints (GRA+), this article provides novel insight into the effectiveness of fifty-fifty compromise, which can inspire decision-makers that unthinkingly conducting the fifty-fifty compromise may not always succeed in eliminating collective bias. Relevant large-scale simulation experiments are conducted in this article to explore when decision-makers may eliminate the bias between the two groups. This article reveals a social paradox: compromise sometimes may not eliminate group bias, and instead, both sides may be offended.

Under the current fluctuating market environment, the production industry is facing increasing complexity in product production decisions. For this concern, this study aims to find an assignment with more stability in the face of random fluctuations. It will explore how to handle random fluctuations through simulation of volatility with respect to the industry chain. This article first formalizes the group role assignment in fluctuation (GRAF) problem and uses the improved environments-classes, agents, roles, groups, and objects (E-CARGO) model for assignment solving. Furthermore, it simulates fluctuations through the Monte Carlo method and uses E-CARGO to solve each fluctuating task allocation problem. After that, it quantifies the allocation using three indicators: local overlap degree, global overlap degree, and profit score, thereby solving the stable assignment problem under fluctuating conditions. Moreover, based on the above three quantitative indicators, this article uses weight to provide decision-makers with multistrategy assignment options in actual production. Finally, different distribution fluctuations show our model's adaptability. This article also conducts large-scale simulation experiments on the selected stable assignments to verify the proposed methods. Through the proposed method, we can help enterprises make better decisions and achieve greater profits in a volatile market environment.

Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to blockchain security. Currently, traditional detection methods primarily rely on static analysis and formal verification, which can result in high false-positive rates and poor scalability. Large Language Models (LLMs) have recently made significant progress in smart contract vulnerability detection. However, they still face challenges such as high inference costs and substantial computational overhead. In this paper, we propose ParaVul, a parallel LLM and retrievalaugmented framework to improve the reliability and accuracy of smart contract vulnerability detection. Specifically, we first develop Sparse Low-Rank Adaptation (SLoRA), a technique for efficient LLM fine-tuning tailored to smart contract vulnerability detection. Distinct from existing LoRA methods, SLoRA inserts parallel sparse and low-rank branches after the attention projection and the feed-forward block, enabling LLMs to capture both global code semantics and localized vulnerability patterns while maintaining low training overhead. We then construct a vulnerability contract knowledge base and develop a hybrid Retrieval-Augmented Generation (RAG) system that integrates Okapi BM25 with dense retrieval to provide complementary lexical and semantic evidence for smart contract vulnerability verification. Furthermore, we propose a meta-learner-based gated verification module to fuse the outputs of the SLoRA detector and the two RAG-based detectors, thereby generating the final detection results. After completing vulnerability detection, we design chain-of-thought prompts to guide LLMs to generate comprehensive vulnerability detection reports. Simulation results demonstrate the superiority of ParaVul, especially in terms of F1 scores, achieving 0.9398 for single-label detection and 0.9930 for multi-label detection.

Aquaculture, as a vital component of the fisheries industry, is assuming an increasingly significant role in meeting the growing global demand for aquatic products. However, the allocation of aquaculture resources has become increasingly complex. Overproduction of a single species can lead to market oversupply, resulting in sharp price declines and substantial profit losses for fishermen. The Environment - Classes, Agents, Roles, Groups, and Objects (E-CARGO) model has shown strong potential in addressing such socio-economic problems. This study extends the Group Multirole Allocation (GMRA) model to formalize and address the Aquaculture Imbalance Problem between Supply and Demand (AISDP). The objective is to maximize total profit while considering market demand, disaster risk, and the potential for oversupply. Extensive simulation experiments reveal that the maximum total profit does not occur at the threshold, but rather beyond it—meaning that even though the unit price decreases, total profit can still be increased by further increasing the stocking quantity. Furthermore, the results suggest that fishermen should select the regulatory parameter based on real-world market dynamics and their risk tolerance. This enables aquaculture enterprises to adopt optimal, diversified decision-making strategies aligned with resource availability and strategic development goals.

The efficiency of Intelligent Transport Systems (ITS) runs on high-quality traffic data, however in real-world deployments, sensors failures, communication interruptions or other issues often lead to missing data, which affects the performance of ITS. Aiming at traffic data’s complex spatio-temporal characteristics, although the latent factorization of tensors (LFT) model has been widely used for missing-value completion, its non-convex objective function makes it difficult for first-order optimization methods to approximate high-quality second-order stationary points, therefore limiting the improvement of the completion accuracy. To address the issues, this paper proposes an incomplete tensor complementation model combining Tucker decomposition and second-order optimization strategy to improve the complementation accuracy and convergence stability. To address the issues, this paper proposes a Second-order Latent Factorization of Tensors based on Tucker Decomposition (SLTD), and efficiently solves it via Gauss-Newton approximation, so that it can significantly improve the model performance while keeping the computational cost low. Experimental results on real traffic datasets (in terms of average vehicle speed) from four cities verify the effectiveness of SLTD. Results show that the proposed model outperforms existing prevailing methods in terms of accuracy and provides a better solution for traffic data completion.

Flexible employment remains a critical topic for crowdsourcing platforms. Decision-makers always focus their attention on maximizing operational efficiency, yet rarely prioritize the work experience of crowdsourcing employees. However, suboptimal working conditions contribute to user attrition, ultimately diminishing platform profitability. While the importance of worker experience in crowdsourcing platforms is recognized, the Flexible Task Allocation Problem (FTAP) has seen relatively little computational investigation due to the absence of powerful quantitative analytical tools. A notable exception is the Environment-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, a mature computational framework with demonstrated efficacy in resolving similar socio-technical challenges. Consequently, this study builds upon the E-CARGO model and its Group Role Assignment (GRA) sub-model to provide a formalization and systematic analysis of the FTAP. By incorporating adjustments for distance and role-switching, the enhanced GRA model can significantly optimize the work experience of crowdsourcing employees, albeit at a slight cost to overall performance. This improvement helps crowdsourcing platforms retain more users and expand their scale. Relevant simulation experiments are conducted in this study to rigorously evaluate the effectiveness of these optimizations.

Complexes from protein-protein interaction (PPI) are one of the fundamental molecular parts to perform a variety of biological functions, being of great importance in studying protein functions and action mechanisms. In this paper, we summarize the common protein sequence coding methods and propose a novel multiple-channel encoding, especially for convolutional neural networks (CNN). The proposed encoding consists of basic sequence information and additional sequence characteristics, such as amino acid contents and local sequence fragments. This new composite encoding provides specific and combined features from the original sequence data to enhance the feature abstraction capability of the CNN model. Results of encoding testing indicated performance improvement of 8.46% than the original SSC encoding method, and 4.13%-10.88% compared with literature methods. In 5-fold cross-validation experiments of 718306 PPIs involved 16470 proteins, the overall performance of the proposed method can achieve an accuracy of 94.35% and 0. 8871 of MCC. The prediction was validated by carrying out molecular docking and enrichment analysis, suggesting potential possibilities of real PPIs. The proposed method may provide new insights into the identification techniques of PPIs and can help improve the PPI prediction method. .

Edge computing allows edge devices to offload computational tasks to edge servers, utilizing various hardware resources for efficient computation. Unlike cloud facilities, edge servers have limited resources. A long-term challenge is to quickly evaluate all the edge server resources and select the suitable server for the task, with high requirements for both processing time and allocation effect. The Edge Resource Allocation Problem (ERAP) represents a typical agent evaluation in collaborative work and falls within the realm of the Group Multi-Role Assignment (GMRA) problem. Based on the GMRA model, we formalize the ERAP as an optimization problem with an improved Edge- GMRA model. Additionally, we investigate the feasibility of an enhanced constraint scheme in the improved model. By boundary detection scheme, we implement quickly eliminated the infeasible solutions within the search range for ERAP. Experimental results demonstrate that the enhanced constraint scheme improves the allocation of high-priority tasks with superior acceleration as the number of agents increases, and the boundary detection scheme performs effectively in scenarios with insufficient server resources. The combination of these two schemes significantly accelerates the solution process, achieving an acceleration ratio exceeding 50%. The proposed dynamic adaptation mechanism with asynchronous agent monitoring and sliding-window threshold adjustment maintains the stability of the system under fluctuation of 15% resources, while our task-type recognition system demonstrates 92. 4% classification accuracy in six workload categories. Extensive evaluation shows the framework sustains sub-100ms decision latency during 80% resource contention scenarios, achieving 23% higher throughput than conventional methods while reducing service-level agreement violations by 41% in dynamic edge environments.

In the logistics chain, through collaboration, multiple supplier enterprises are able to achieve resource sharing, thereby offering a broader and more stable service scope while enhancing risk resilience. However, despite these benefits, differences in the distribution capabilities of various supplier enterprises can lead to inconsistencies in the overall service quality of distribution tasks. Therefore, in collaborative distribution, it is crucial to ensure the overall service quality while maximizing benefit. To address this challenge, this article formalizes the collaborative distribution problem (CDP) in the logistics chain via the environments — classes, agents, roles, groups, objects (E-CARGO) model. A novel solution is designed for CDP by extending the group multirole assignment (GMRA) model. This solution incorporates the qualification matrix adjustments (QMA) algorithm to systematically prioritize suppliers based on their qualifications, thereby maximizing benefit while ensuring the overall service quality of collaborative distribution tasks. Large-scale random experiments show that the proposed method effectively balances the tradeoff between the benefit and overall service quality in the CDP under various data distributions. Moreover, decision-makers can obtain an optimal assignment solution through the Pareto front.

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.

The audit process is a critical component of any company's financial management. It aims to guarantee that the financial statements of a company are accurate, reliable, and in compliance with relevant laws and regulations. Additionally, auditing provides an opportunity for companies to pinpoint areas for improvement, which can help increase efficiency and reduce instances of financial fraud and corruption. As lots of companies with small scales choose to perform the audits externally rather than relying on an internal team, the evaluation of the performance has become a significant problem during building an auditor team. Meanwhile, efficiency is another factor to pursue. This paper formalizes this problem with the Group Multirole Assignment with the Cooperation and Conflict Factors (GMRACCF) model. Specifically, we propose a new method to evaluate the efficiency factors and build a cooperation and conflict factors (CCF) matrix by turning it into time CCFs between auditors. Therefore, with several experiments being conducted, we can know that time factors can have different impacts on the performance of the External Auditor Assignment Problem (EAAP) depending on how much emphasis has been placed on its influence.

The development of the Internet has led to the rapid development of a new business model called crowdsourcing. However, increasingly complex crowdsourcing tasks are difficult to be decomposed and decoupled by the performers in the actual execution. The crowdsourcing platform needs to provide a detailed task assignment method to solve this problem. At the same time, crowdsourcing tasks may involve user privacy, and protecting private information from being known by others also needs to be considered by the platform. While completing the crowdsourcing task, considering task fairness and team fairness to improve the reliability of task completion and the fairness perception of crowdsourcing members. Therefore, this article formalizes the crowdsourcing team assignment problem through Environments – Classes, Agents, Roles, Groups, Objects (E-CARGO) model. Introducing information security constraints to construct a new sub-model (GRAINS) to solve the crowdsourcing team assignment problem. On the premise of obtaining the optimal performance assignment, the two types of fairness are discussed, providing a new decision-making scheme for the crowdsourcing platform. Through large-scale random simulation experiments, it is proved that the model can improve task and team fairness while ensuring the overall performance of the task, and quantitatively analyze the partial performance for fairness sacrificed.

Although public transportation brings more and more convenience and practicality, it also presents greater safety hazards and economic concerns. How to select reasonable bus driver rostering (RBDR) for bus companies has become a pivotal resource optimization issue in public transportation by extending the Group Multirole Assignment (GMRA) model, this paper formalizes such a problem. Moreover, we propose multi-criteria decision making as a new method for driver evaluation, incorporating agent capability and satisfaction as important criteria. Additionally, in order to find the result solution more reasonably, the parameter change rules are obtained through multiple simulations. We first use the slope to find the steep drop point, and then use the variance and range to find the balance point, thereby obtaining the target parameter combination. The staged parameter selection method improves operating efficiency. Large-scale simulations indicate that the improved GMRA algorithm is suitable for different scenarios and can return multiple parameter combinations. By using this method, bus companies are able to select optimal parameter combinations in order to make diversified decisions based on transportation resources and development strategies.

Everybody knows that China has put forward the concept of "carbon peaking and carbon neutrality" in response to climate change. This paper proposes a novel perspective from the standpoint of the Role-Based Collaboration (RBC), employing the Environment-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model and the Group Multirole Assignment (GMRA) model to construct an energy supply strategic planning (ESSP) that simulates the progressive carbon peaking process in order to compare and analyze the current carbon peaking target of China. Secondly, we propose a simple method to evaluate carbon peaking schemes, and conduct simulation experiments for different schemes. It is concluded that Chinese current carbon peaking target is relatively conservative, projecting achievement by 2027 with an estimated 19.7 billion tons of CO2 emissions, while having the lowest cost per unit of emission reduction. Furthermore, a significant reduction of 66.08% in carbon intensity is projected for 2030 compared to 2005, which is 1.08% higher than the original target.

The Short Video Assignment Problem (SVAP) is one of the main problems short video platforms face. This article suggests converting SVAP into a Many-to-Many Assignment Problem, which the Group Multirole Assignment can specify, to obtain the best video assignment plan as much as possible. GMRA is a sub-model of the E-CARGO, transforming the relationship between video classes and servers into the relationship between agents and roles. To improve the privacy and security of data as much as possible, this paper uses the method of Federated Learning (FL) to process data. The Entropy Weight Method (EWM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods evaluate video classes in multiple dimensions and indicators to assess their matching degree with servers. Finally, the linear programming method was used to solve the problem, and the best group performance is 985.86639

In this article, we tackle the dual challenges of efficiency and quality in music generation. We aim to create a model that produces high-quality music efficiently while keeping the model lightweight and the music authenticity and creativity. This research proposes a model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for music generation. LSTM units capture long-term dependencies in music sequences. CNN units are included to extract musical features, enhancing the model’s grasp of structure and style. We train and valid the model on the ADL Piano MIDI datasets which is a sub set of the Lakh MIDI datasets. Finding that our CNN-LSTM model excels in music creation. It reduces computational complexity, boosting operational efficiency. This paves the way for deployment on resource-limited devices like mobiles or edge computing platforms. Combining these methods ensures the model learns form rich musical features, generating coherent, stylistically consistent pieces.

Most project development processes are iterative and can be divided into multiple tasks. One person can take on multiple tasks, and one task can be assigned to multiple people. The many-to-many personnel allocation method greatly improves the efficiency of the project and saves the cost of the project. There will be two different losses in this allocation plan: the tasks undertaken by personnel are too discrete and the personnel are easily distracted when undertaking important tasks. This paper first formally models the project personnel allocation problem through the Group Multi Role Assignment (GMRA) model. Then two new constraint formulas were proposed to extend the GMRA model to reduce the loss of personnel allocation and the necessary and sufficient conditions of the extended method were proved. Subsequently, two large-scale simulation experiments were carried out to compare and demonstrate the differences between the expanded new method and the original model, and to explore the sufficient and necessary conditions to increase the speed of finding feasible solutions for the new method. Using the improved model for arranging personnel of development projects not only enables efficient many-to-many allocation but also helps reduce a lot of hidden losses in the project process.

Courier delivery is the end of the supply chain and affects the final delivery of products. Due to the promulgation of new courier delivery regulations, home delivery services have become the main choice for consumers, which has resulted in a greater workload. How to Reasonably Allocate Couriers for LEan Management (RACLEM) in supply chain optimization poses a challenge to traditional logistics. By extending the Group Multirole Assignment with Efficiency Degradation (GMRAED), this paper formalizes the problem. Moreover, we propose a quantitative calculation method of efficiency degradation based on Amdahl's law, taking region similarity and the number of tasks as important criteria, and compare the performance of different personnel arrangements. Additionally, compared with the brute force algorithm, we combine GMRAED and Genetic Algorithm (GA) to create a practical solution. Large-scale simulation experiments demonstrate that the genetic algorithm significantly shortens the solution time, and the lowest experimental accuracy is 99.407%. By using the above method, decision-makers are able to assist companies optimize personnel resource allocation, minimize personnel waste, and improve the performance of courier delivery within a shorter timeframe.

The Bank Credit Decision-Making Problem (BCDMP) is one of the main issues that bank operations need to face. To obtain the maximum profit value and optimal loan plan of the bank as much as possible, this article suggests converting BCDMP into a Many-to-Many Assignment Problem, which can be specified by the Multi-Role Assignment (GMRA). GMRA is a sub-model of the E-CARGO. By revised GMRA, the relationship between the enterprises and loans is converted into the relationship between agents and roles, and a multi-dimensional and multi-index evaluation method is used to evaluate the matching degrees between enterprises and loans. We use the Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to obtain the enterprises' score for a loan through four indicators: profits, inventory, turnover ability, and credit. Then, we considered the impact of different loan interest rates on bank profits, obtained enterprise scores under different loan interest rates, and used linear programming to solve the problem, achieving good results (The bank achieved a profit margin of 5.43176% via revised GMRA).

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.

Role negotiation (RN) is situated at the initial stage of the role-based collaboration (RBC) methodology and is independent of the subsequent agent evaluation and role assignment (RA) processes. RN is to determine the roles and the resource requirements for each role. In existing RBC-related research, RN is assumed to be static. This means that the roles and the resource requirements for each role are predetermined by decision-makers. However, the resources allocated to each role can vary. At this time, iterative RN outcomes will have different RA results. There may not be a direct dominant relationship between different RA outcomes, especially when solving group role assignment (GRA) with multiple objectives (GRA++) problems, which makes it even more complex. To address these concerns, we introduce the original bilevel GRA++ (BGRA++) model. Specifically, at the lower level of BGRA++, a strategy is designed for quantifying iterative RNs. For the upper level, we introduce the novel GRA-NSGA-II algorithm for the RA process. Finally, we introduce the concept of decision tolerance to assist decision-makers in selecting the optimal solution from the multiple RNs. Last, simulation experiments are conducted to verify the robustness and practicability of the proposed method. Comparisons and discussions show that the proposed solution is highly competitive for solving the GRA++ problem with iterative RN.

In collaborative endeavors, there are always various types of cooperation, conflict, and public interest relationships. Taking home appliance production as a typical example, this article extends the group role assignment with the cooperation and conflict factors (GRACCF) model by newly adding public interest relationships to formalize the problem dealing with the above three collaboration relationships. By adding three different threshold parameters to represent cooperation, conflict as well as public interest relationships, we can make complex decisions based on the benefit values from different relationships. Through the discussion and analysis of large-scale experiments, different change trends are presented to help make certain adjustments and provide suggestions on the boundaries of these values in production and formulate appropriate production plans after determining these boundaries.

The educational equality or equity problem (EEEP) has a long-term debate in civilization. This problem is often discussed by sociologists, philosophers, educationists, and so on for various phenomena or decisions. However, there is hardly any absolutely convincing answer to this question. We believe that a series of quantitative methods may provide decision-makers with a clearer orientation in their discussions and help them make decisions. Therefore, this article depicts a common scenario to discuss the EEEP. It uses quantitative methods to conduct computational social simulations and obtain the results under equal or equitable conditions via role-base collaboration (RBC), environments-classes, agents, roles, groups, and objects (E-CARGO), and group role assignment (GRA). The simulation results show that educational equality or equity can be significantly improved by making a reasonable and optimized allocation plan through GRA. Finding the optimal allocation plan, which validates the reliability of the results by changing the experimental parameters of equality and equity, is helpful and interesting to relevant decision-makers.

In the production process of automobiles, parts procurement is invariably a crucial step. In order to find an optimal decision, it is a challenge to match parts to suppliers for the limited financial and material capabilities of every supplier. This paper formalized the problem by Group Multirole Assignment (GMRA). Meanwhile, the success of this assignment process depends on the choice of the agent evaluation method. It depends on the industrial chain data, which can acquire feature indexes of parts from previous purchase records. Furthermore, comprehensive evaluation of parts procurement bases on multiple factors. Thus, it is difficult to reflect different quantifications using the multifactorial parameter semantics. Therefore, we propose a new method of Fuzzy Hierarchy Comprehensive Evaluation (FHCE), using membership grades of the fuzzy theory to differentiate the parameter and the weight, which can use objective quantitative analysis to optimize procurement plan. After that, based on GMRA, decision makers are able to maximize the resource utilization ratio to determine optimized solutions when funds or part types are limited. Simulation experiments indicate that the proposed method is efficient and feasible, which is verified practicable.

The phenomenon of uneven product quality in software outsourcing enterprises is common. Even for products with similar functionality, the company cannot guarantee the final delivery quality. Effectively solve the stability of product quality, which is more conducive to long-term cooperation between customers and the company. Hence, this paper formalizes the Outsourced Products Task Allocation (OPTA) problem via the Environments-Classes, Agents, Roles, Groups, Objects (E-CARGO) model. Through its sub model Group Multirole Assignment (GMRA), a team with the optimal qualification value can be obtained. However, the stability of the assignment result is still not guaranteed. Therefore, this paper innovatively introduces the concept of variance and proposes the Group Multiple Role Assignment with Considering Variance (GMRACV) model to tackle this issue. And make better improvements to it, achieving a performance loss of 1% in exchange for about 32% stability. Large-scale randomized experiments show that the proposed model can effectively reduce the variance between products while maintaining the overall quality of all products. And for data with different distributions, the model can still obtain excellent results stably, which further verifies the feasibility of the model.

Although cloud manufacturing offers greater flexibility and diversity than traditional manufacturing, it also presents greater uncertainty and variability. How to select stable cloud providers for material procurement (SSCPFMP) has become a critical supply chain optimization issue in cloud manufacturing. By extending the Group Role Assignment (GRA) model, this paper formalizes the problem. Moreover, we propose a new method for evaluating cloud providers that incorporates stability as an important criterion. Additionally, in order to complete the stability assessment as quickly as possible, we propose using the KB4 logic instead of the commonly used KB5 to mine potential cooperative relationships between cloud providers. We prove by deduction that KB4 and KB5 are equivalent. Largescale simulation experiments indicate that the KB4 logic performs significantly better than the KB5 logic, which can be improved by up to 43.78%. By using this method, decision makers are able to find more stable cloud providers for material procurement within a shorter timeframe.

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.

In mass crowdsourcing, a platform is used for task allocation. Large complex tasks may be assigned to teams directly. To ensure the rapid accomplishment of tasks, we need to assign a task to multiple teams, while a team is composed of at least one worker. On the other hand, in order to ensure the enthusiasm and income of workers, the platform allows one worker to participate in a limited number of teams. Thereby, a team may undertake multiple but limited tasks. It is obvious that task allocation needs to avoid each worker being overloaded as well as prevent information leakage, which is caused by a task assigned to different teams at the same time. Therefore, different teams being assigned to a certain task cannot include the same workers. Such a scenario forms a many-to-many (M2M) assignment problem [or group multirole assignment (GMRA)] under high-order cardinality (HC) constraints, while the platform wants to choose appropriate and excellent teams for all the tasks in a specific time window. In order to solve this problem, this article studies and formalizes the above problem by introducing HC and conflicting agents on roles (CAR) constraints to GMRA. The main contributions of this article include: 1) the first formalization of the team allocation problem (TAP) in crowdsourcing through extending GMRA and the creation of a composition matrix to express HC constraints of agents and conflict avoiding constraints; 2) theoretical proofs of the theorems of the formalized problem, such as a necessary and sufficient condition (NSC), which confines the solution space of the problem; and 3) a practice solution to the proposed problem based on the IBM ILOG CPLEX optimization package (CPLEX). All the proposed approaches are verified by simulation experiments, which demonstrates that the proposed approaches are efficient, feasible, and practicable.

The ever-increasing web application programming interfaces (APIs) in various service-sharing communities (e.g., ProgrammableWeb.com and Mashape.com) have enabled software developers to quickly create their interested mashups conveniently and economically. However, the big volume of candidate web APIs and their differences often make it hard for software developers to discover a set of appropriate web APIs for mashup creation by considering API functions and API quality performances (e.g., popularity, compatibility, and diversity) simultaneously. These decrease the mashup development success rate and the mashup developers’ satisfaction significantly. In view of these challenges, a novel web APIs’ recommendation method named the popularity-aware and diverse method of web API compositions’ recommendation (PD-WACR) is proposed in this article. In concrete, we model web APIs’ functions, popularity, and compatibility with an API correlation graph. Afterward, correlation graph-based web APIs’ recommendation is performed with popularity and compatibility guarantee. Moreover, a top-$k$strategy is adopted in the recommendation process, so as to diversify the final recommended web APIs’ results. Finally, massive experiments are carried out on a real-world web API dataset crawled from ProgrammeableWeb.com. Experimental comparisons with related methods show the advantages and innovations of the proposed PD-WACR method.

Timing hot gas defrosting is one of the commonly used methods of cold storage defrosting, it only needs to determine the time period interval and duration. However, this method is difficult to arrange a reasonable defrost time period according to the different power of the air cooler and the type of industrial power time period, resulting in a waste of electricity. Thereby, this paper tackle this issue using the Group Role Assignment (GRA) model to formalize the defrosting period allocation with flexible intervals problem (DPFI) of the air cooler. On the one hand, it innovatively uses periods instead of discrete individuals as agents for the allocation of defrosting periods. On the other hand, according to the actual frosting situation of the air cooler, different period intervals are adopted to defrost the air cooler. The focus of this article is to express the defrosting period interval constraint linearly. The formalization of GRA makes it easy to find a solution through integer programming (IP) using the PuLP package of Python. Large-scale simulation experiments verify the robustness and practicability of the proposed method. In addition, by comparing the power cost of defrosting at different period intervals and the same period interval, simulation experiments show that using flexible period intervals to defrost the air cooler can significantly reduce the power cost of defrosting.

Different new employees often have different wishes to take part in different departments. Personal wishes are a significant issue in human resources management. Decision makers need to not only assess employees’ job skills, but also consider their personalized choices with respect to the job posts. How to balance the workers’ wishes with the group performance is a challenge. This paper formalizes this problem via the Group Role Assignment with Balance (GRAB). Through a compensatory assignment based on GRAB, an interesting and successful approach is proposed, which can balance wishes and performance in a state of equilibrium. Simulation experiments indicate that, the proposed method is efficient and feasible, which is verified practicable.

To make full use of the resources in production, the orders are usually split, decoupled, and reassembled to series new orders, which often lead to a new complex high-order set of tasks. Traditional processing methods are to first produce some portion of the order, and then manually adjust the rest production plan. Such a method may cause over-production. Also, when tasks are urgent, it is difficult to control the waste rate of emergency raw materials due to quick responses to the urgent requests without careful planning. It is not a trivial work to guarantee efficiency and emergency of production in this case. Therefore, this paper formalizes the high-order set assignment problems (HOTP) by using group role assignment (GRA). Based on GRA, this paper proposes a role negotiation method by using the Hierarchical clustering and Analytic Hierarchy Process (AHP) algorithms. The formalization of HOTP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). Based on the proposed method, decision makers can optimize production resources and assign them in place at one time, minimize the waste and ensure the productivity. The proposed approaches are verified by simulation experiments and demonstrated to be efficient, reasonable and practicable.

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.

Portfolio creation and management are fundamental and important investment services. An investment manager is often responsible for dealing with many clients. Thus, s/he needs tools appropriate to creating, managing and matching portfolios with clients, according to market conditions. In planning, one financial product can be assigned to many, but different clients and one client may buy many, but different products. It is in fact a higherorder investment problem that involves many to many (M2M) assignment in the process. The manager must establish a mix of products, for various levels of return, that suit a client's risk tolerance. This paper solves the higherorder M2M assignment problem via the higher-order group multi-role assignment (HO-GMRA). Based on the concise formalization of Role-Based Collaboration (RBC) and its E-CARGO model, a practicable multi-object optimization approach is proposed, and its kernel is an x-ILP (extended Integer Linear Programming) planning method. Method verification is achieved by simulation experiments with respect to a real-world problem. The experimental results demonstrate the practicability of the proposed solutions.

Medical resources of physical examination (P.E.) are often insufficient. The gap between providers and demanders are always existing and becoming more and more sensitive in some densely populated areas. If resources of P.E. departments are regarded as nodes, then each path selected by patients will constitute a small world network. Furthermore, with respect to traditional research concentrating on the feature between nodes in network, adaptive collaboration (AC) is in fact an important method to improve the group performance of the whole system in the small world network. Based on these, this paper deals with this kind of problem with respect to the scenario of P.E., which attempts to help decision makers of the health center to schedule limited resources and improve a patient's satisfaction and the system performance. It firstly abstracts a medical examination by Role-Based Collaboration (RBC) and its general model E-CARGO. The adaptive collaboration model is constructed by system states and optimized via series of group role assignments (GRAs), which is a subtask of RBC, and it can be accomplished by linear programming. All the proposed methods are verified by simulation experiments, and the team performance is improved via adaptation of the assignment strategies, which provides a new insight into the small world study.

Human age estimation from face image has drawn increasing research attention due to its many meaningful applications such as demographics analysis and surveillance monitoring. However, most existing methods directly extract age-specific features for age estimation and ignore age-related gender information. In this paper, we propose a simplified deep learning network for age estimation by simultaneously learning aging and potential gender features. Specifically, we first learn the potential gender information from face images. Then, we employ a two-stream convolutional neural network to simultaneously learn and concatenate the aging and gender latent appearance features. Third, we feed the multi-type features into a compact convolution network, named AgeNetwork, to further learn the age-specific features. Finally, we use a deep regression function to estimate the detailed ages. Extensive experimental results demonstrate the promising effectiveness and efficiency of our proposed method in comparison with state-of-the-arts.

Oil resources are becoming increasingly scarce. Pure electric vehicles have huge advantages, in terms of energy efficiency and emission reduction. In cities, the locations of required charging stations and the number of required charging piles are determined according to the traffic flow on a road. Unreasonable allocation not only creates safety problems due to high electrical loads, but also increases the cost of the placements. Such allocations will involve the many-to-many (M2M) assignment in the process, which is necessary to establish an optimal model for distributing. Thus, this paper formalizes the charging pile siting problem (CPSP) via the group multirole assignment (GMRA) model, which is one of the most important methods to deal with the M2M problem. Based on GMRA, this paper proposes a role negotiation method by using a spectral clustering K-Means++ Algorithm based on location. The formalization of GMRA makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX) via the Integer Programming (IP). All the proposed approaches are verified by simulation experiments, which have been proved to be efficient, feasible and practicable.

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.

Hashing has been widely studied for cross-modal retrieval due to its promising efficiency and effectiveness in massive data analysis. However, most existing supervised hashing has the limitations of inefficiency for very large-scale search and intractable discrete constraint for hash codes learning. In this paper, we propose a new supervised hashing method, namely, Discrete Semantic Matrix Factorization Hashing (DSMFH), for cross-modal retrieval. First, we conduct the matrix factorization via directly utilizing the available label information to obtain a latent representation, so that both the inter-modality and intra-modality similarities are well preserved. Then, we simultaneously learn the discriminative hash codes and corresponding hash functions by deriving the matrix factorization into a discrete optimization. Finally, we adopt an alternatively iterative procedure to efficiently optimize the matrix factorization and discrete learning. Extensive experimental results on three widely used image-tag databases demonstrate the superiority of the DSMFH over state-of-the-art cross-modal hashing methods.

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.

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents' abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role's ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical.

Ensemble classifier, b y combining multiple classifiers, can often achieve better performance than single classifiers in intrusion detection. Although some ensemble methods have been used for intrusion detection, most of them directly fuse detection outputs after multiple classifiers a re generated. It potentially reduce the overall performance and flexibility. Aiming at achieving a high-precision intrusion detection model with good generalization performance and robustness, an error-correcting ability based collaborative multi-layer selective classifier ensemble model is proposed in this paper, named ML-SCEM. In the ML-SCEM, a novel multi-layer structure consisting of 5 continuous layers is designed, each layer of which is equivalent to a binary classification. In each layer, an error-correcting based selective classifier ensemble method(SCEM) is used to select the main classifier and error-correcting components from M preselected base classifiers to generate an ensemble classifier suitable for this layer classification category. Furthermore to improve time efficiency a nd detection performance, the original dataset is divided into 3 parts of TCP, UDP and ICMP according to the network protocol, so that the three parts are collaboratively detected. The performance of the proposed ML-SCEM is evaluated and compared on the NSL-KDD dataset. It achieves accuracy of 97.07%, false positive rate of 1.58% and efficiently detects various types of attacks.

Doctors' personalized outpatient scheduling is an important and challenging problem, which is due to the existence of spatio-temporal constraints, and resulting in difficult to meet the willings of physicians. In order to improve the satisfaction of physicians and promote the quality of diagnosis and treatment, this paper formalizes the doctors' personalized outpatient scheduling problem by the Many to Many assignment with spatio-temporal constraints. Based on a general outpatient arrangements scenario of a Class A tertiary comprehensive hospital in Guangzhou, this paper takes the treatment time slot as the role requirement, models the assignment with spatio-temporal constraints, and conducts linear programming (LP) via Role-Based Collaboration and its E-CARGO model. Therefore, the formalized problem can be solved by using the IBM ILOG CPLEX package (CPLEX). The results of simulation experiments present the practicability, accuracy and efficiency of the proposed solution based on CPLEX in this paper. The proposed approach can achieve the best scheduling effect and score, and ensure the highest satisfaction of physicians to promote services.

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.

Role assignment is a critical element in the role-based collaboration process. There are many factors to consider when decision makers undertake this task. Such factors include a decision maker's preferences and the team's performance. This paper proposes a series of methods, relative to these factors, to solve the group role assignment with balance problem through an association with the one clause at a time approach that is a well-accepted and logic-based association rule mining method. The proposed methods are verified by simulation experiments. The experimental results present the practicability of the proposed solutions. Using the proposed methods, decision makers need only to establish coarse-grain preferences. The fine-grain preferences can be mined. Furthermore, a balance is obtained between the fine-grain preferences and the team's performance.

Role assignment is a critical element in the role-based collaboration process. There are many different requirements to be considered when undertaking this task. This correspondence paper formalizes the group multirole assignment (GMRA) problem; proves the necessary and sufficient condition for the problem to have a feasible solution, provides an improved IBM ILOG CPLEX optimization package solution, and verifies the proposed solution with experiments. The contributions of this paper include: 1) the formalization of an important engineering problem, i.e., the GMRA problem; 2) a theoretical proof of the necessary and sufficient condition for GMRA to have a feasible solution; and 3) an improved ILOG solution to such a problem.

Nowadays, as the development of mobile communication, it is very important to serve users. Because of the subjectivity of user experience, the data of user experience has deviation. In the paper, a cooperative modeling method based on the improved Support Vector Machine is proposed, which can evaluate the quality of experience by using measurement report. The results of the experiments show that our method is effective to calculate the quality of experience.

Natural disasters can lead to severe losses in human life and property. Because many factors combine in a disaster, such events are difficult to forecast accurately. A cooperative multi-classifier method is proposed in this paper to mine local area meteorological data. The proposed method is verified by the implementation of both base and integration classifiers. Experimental results indicate that our proposed method has higher classification accuracy and faster grouping ability compared with conventional classifiers.

Role-Based Collaboration (RBC) is a computational thinking methodology where roles provide an underlying mechanism to facilitate abstraction, classification, separation of concerns, dynamics, and interactions. From a meta theoretical perspective, the specification of groups, roles and agents is a critical element of RBC. In consideration of the relationships and hierarchies faced by groups, roles and agents, we propose a minimal role playing logic system (MRPL) through substructural logic, which is polynomial in complexity, in support of RBC. From MRPL and RPLs extending from it, there are three levels of application, i.e., the global level governing how people organize agents to form a group; the concatenative level for role assignment with respect to logic and algebra, and the operational level governing properties, relations and structures that should appear in collaborative system design. From MRPL to RPLs, one can extend it to suit other appropriate applications.

Temporal characters due to the alteration of salary policy and salary standards are discussed in this paper. According to these characters, the authors put forward a formalization inference model of temporal logic and present a discussion of the knowledge database driven by temporal knowledge, called the temporal-driven knowledge database.