As an emerging network technology, Network Function Virtualization (NFV) enables network functions decoupling from dedicated hardware by replacing traditional middleboxes with software implemented Virtual Network Functions (VNFs). In NFV-enabled Internet of Things (IoT) networks, each IoT service can be represented as an ordered sequence of VNFs, referred to as Service Function Chain (SFC). Through NFV, operating expenditure and capital expenditure can be significantly reduced, thereby achieving flexible provisioning of IoT services. However, with the arriving of 6G era, the network scale of IoTs continuously expands, and service requirements of IoT users become more diversified. Particularly, 6G enabled IoT services have stringent delay requirements. How to efficiently place the SFCs in multi-domain IoT networks to satisfy the specific delay requirements while guaranteeing quality of service becomes a serious challenge. To this end, in this paper, we investigate the problem of delay guaranteed SFC placement in multi-domain IoT networks. Specifically, by taking in account QoS requirements and VNF dependency relationships, we formulate the problem of delay guaranteed SFC placement in multi-domain IoT networks as a multi-objective optimization model to maximize service acceptance ratio and minimize operational cost, while satisfying the delay requirements of SFC requests. To solve the problem, we further design a Delay Guaranteed heuristic SFC Placement (DGSP) algorithm with VNF parallelization. In the proposed DGSP algorithm, the VNFs without dependency relationships are placed in parallel in an adaptive and cost efficient manner, and virtual link mapping is performed based on the shortest path algorithm. Finally, we conduct simulation experiments for performance evaluation, and simulation results demonstrate the proposed DGSP algorithm can get higher service acceptance ratio and lower operational cost than comparison algorithms.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Axelrod's model and its subsequent studies have become a valuable framework for fostering cooperation norms among self-interested agents. Within this framework, the concepts of "boldness" and "vengefulness" are specifically employed to characterize agents' behaviors in terms of cooperation and punishment (including metapunishment). Describing behavior solely through the parameters B and V may be overly simplistic and lacks generalizability, making it difficult to apply to other scenarios. Moreover, privacy concerns and the difficulty of evaluating complex states in real-world scenarios limit agents' access to detailed payoff information from their neighbors. To address these questions, our paper employs self-regarding Q-learning, a well-established method for examining the dynamics of strategy updates and agents' learning processes, to investigate whether metanorms can naturally emerge through players' strategy selection. Through extensive experiments, we observe cooperative norms' successful emergence driven by agents' strategy selection variations. Over 90% of agents choose to cooperate on average. In subsequent analyses, we explore the underlying reasons for the emergence of cooperative norms from perspectives of changes in Q-values, punishment and metapunishment frequencies. Additionally, we examine the impact of topological structures on players' strategy selection and assess the emergence of norms across different temptation levels, population sizes, and regulatory intensity levels to validate the model's sensitivity.

Subsidies are used to further control the propagation of epidemic security risks as they offer users incentives to practice security investment behaviors. However, previously proposed subsidy policies have typically been studied without insurance, and it is still challenging to build effective subsidy strategies when users can transfer some of the infection loss to the insurer by purchasing insurance, because these external incentives may alter users' decisions to purchase insurance. To this end, we designed three subsidy policies in a scenario where purchasing insurance is one of the available strategies to users: under SPIns and SPInsp, the subsidies are used as the insurance funds and the risk precaution aspect of the insurance funds, respectively, and under SPInsd, a fraction of the subsidies are used to support some free-riding users with high node importance in their purchase of insurance, and the remaining subsidies are used as the precaution aspect of the insurance funds. The subsidy policies are studied with a formulated security investment game model on scale-free networks from the perspectives of the public and insurer. The results show that SPInsd can always work effectively and outperforms others in limiting the extent of an epidemic outbreak with similar or lower social costs, and without a reduction in profit for the insurer. More importantly, an interesting phenomenon emerges in the SPInsp scenario: under specific insurance parameters, the increase in subsidy funding has a negative impact on preventing the risk spread, leading to larger final epidemic sizes. In addition, the effectiveness of SPInsd on scale-free networks with larger average degree or network size and random networks are also studied. We anticipate this work can provide useful insights for policy makers with respect to design and implementation of optimal subsidy policies related to the control of epidemic security risks under an insurance scenario.

信息技术的快速发展和在各行业的广泛应用,对计算机类人才的能力提出了新的要求.该文从新工科建设的角度出发,对计算机学科建设和人才培养问题进行了全方位探析,提出应首先通过思想教育,使学生和教师树立正确的意识,建立多维化的目标体系,再建立完整的制度保障体系和监控体系,确保培养目标的实现,同时应利用信息化教学手段提升教学质量和效率.

Since many classifier methods cannot identify and remove redundant observations and unrelated attributes from data, they usually give more inconsistent classification between actual and predicted outputs. Introducing single- or multi-kernel functions to classifier models helps to solve non-linearly separable problems, but it reduces the predictive interpretability. In this paper, we put forward a novel two-stage sparse multi-kernel optimization classifier (TSMOC) method under the framework of combining support vector classifier (SVC) and multiple kernel learning (MKL), aiming to solve the above issues. With our defined row and column multi-kernel matrices, the proposed method employs iterative updates to compute the ℓ0- norm approximations of coefficients and weights, which extract important observations and attributes besides prediction. Based on the experimental results on thirteen real-world datasets, TSMOC generally outperforms the other seven classifiers of SVC, ℓ1- norm SVC, least-squares SVC, LASSO classifier, SimpleMKL, EasyMKL, and DeepMKL. Besides obtaining the best classification accuracy, TSMOC extracts the smallest number of observations and attributes important to prediction and it can provide explainable prediction with their contribution percentages.

In most of the studies focusing on the conformity of voluntary vaccination decisions, the conformity was always directly modeled as a conformity-driven strategy-updating rule. However, the utility of an individual can also be influenced by the group identity or discrimination behaviors associated with strategy conformity in realities. Thus, a novel utility model of the vaccination game is first formulated in which the influence of strategy conformity is considered. Then, we use the spatial evolutionary game theory to study the dynamics of individual vaccination strategies under the influence of strategy conformity on the scale-free network. The results show that moderate strategy conformity and a high herd immunity threshold have a significant positive effect on vaccination behaviors when the initial vaccination fraction is low. Moreover, for a high initial vaccination fraction, the strong strategy conformity and high herd immunity threshold are more conducive to encourage vaccination behavior. To analyze the model sensitivity, experiments are conducted in the small world network and square lattice network. In addition, we performed the sensitivity analysis on vaccination effectiveness. Finally, the generality of strategy conformity effect is investigated when the myopic strategy updating rule is adopted in the whole population. The result shows that vaccination behaviors can also be promoted under the condition of moderate strategy conformity and low initial vaccination fraction.

With the development of information technology, the infrastructure between enterprises and the connections between businesses show complex network characteristics. The security investment made by an enterprise in a network has an impact on its neighbors, but also bears the impact of its neighbors' security investments. Such individual interaction problems are often modeled as interdependent security games (IDS). In this paper, we study IDS models under three different attack scenarios: the total effort, the weakest link and the best shot. We use evolutionary game theory to explore the dynamics of social payoffs and the average social investments of these three models under different network topologies. The results show that under the total effort model, individuals are more inclined to increase their investments, while under the weakest link and best shot models, individuals choose to minimize their investments due to selfishness. Finally, we study the cluster effect under different network structures and find the threshold at which it exists. (C) 2021 Elsevier Inc. All rights reserved.

In social networks, resource sharing behaviors always take place in groups of individuals and rely on voluntary cooperation. In this work, first, a multi-player donor recipient game in which strategies describe individuals' varying degrees of willingness to share resources is formulated, instead of using the limited binary decisions (e.g. share or not share) in a classical donorrecipient game. Second, the evolutionary dynamics of individual strategies are explored under the influence of two contribution-based resource allocation mechanisms: the total contribution-based allocation mechanism (TCAM) and the direct contribution-based allocation mechanism (DCAM). The results indicate that the network is dominated by the full-cooperation strategy when the cost-to-benefit ratio of resources is not too large and the DCAM is more effective than TCAM. Furthermore, the underlying reason why some strategies with higher sharing willingness can coexist in specific situations, is also explained in detail by leveraging macroscopic and microscopic perspective analysis. Finally, the influences of slandering and whitewashing behaviors conducted by a few malicious individuals on the allocation mechanisms are also studied. Current research will offer new insights into understanding the influence and optimizing the resource allocation policies in social networks.

In social and biological systems, besides interaction payoffs, individual fitness which is used in strategy evolution, can also be influenced by other extrinsic factors. In this paper, individual influence is introduced into the spatial prisoner's dilemma game, which is dependent on the adjustment sensitivity, the duration of the current strategy and strategy imitation behaviors during the game. Tuned by the individual influence, the fitness of some individuals with the same interaction payoffs may be different. The effect of individual influence on improving cooperation has been researched, and the result shows that the level of cooperation is effectively promoted when the individual influence is considered. Moreover, with the adjustment sensitivity becomes larger, a greater degree of cooperation can be warranted even the temptation to defect is relatively large. The role of individual memory length threshold has also been investigated. Interestingly, individual memory length is not the primary factor that enforces the emergence of cooperative behavior among selfish competitors, it only accelerates the velocity of population evolution. By configurational analysis, the reason for the improvement of cooperation level has been carefully explained. It occurs mainly because some cooperator clusters can resist the invasion of defectors by interacting with defectors that have lower individual influences. Finally, the robust with respect to the cooperation evolution in other network structures is further studied. We find out that scale-free and small-world networks make the system as efficient as lattice network. Our work provides a new perspective on understanding of cooperative behaviors in societies.

A high degree of energy efficiency and real-time for data transmission is required in cyber-physical systems (CPSs). Data aggregation is an efficient technique to conserve energy by reducing the amount of transmission data. To optimize real-time communication under constraints of power consumption and data aggregation performance of each node in CPS, this paper presents a learning automata (LA)-based degree-bounded bottle-neck data aggregation tree (DBBDAT) construction framework to minimize the maximum delay on data aggregation trees with bounded degree, which is an NP-hard problem. We model the network of CPS as a connected weighted and directed graph to form a network of LA. Degree-bounded data aggregation trees are constructed first by the action selection of each automaton. Then, the action vector of each automaton is updated by linear reward-inaction learning algorithm, and at last DBBDAT is constructed based on a threshold. Simulation results show that our approach significantly outperforms integer linear programming (ILP)-based method in terms of time complexity. Compared with ILP-based method, it can obtain an optimal solution or a suboptimal solution with guaranteed approximation ratios, and can control the trade-off between accuracy and cost by choosing appropriate learning rate and threshold. Its distributed implementation is simple and it can efficiently solve the problem for the sparse graph in practice.

Adversaries in two-party computation may sabotage a protocol, leading to possible collapse of the information security management. In practice, attackers often breach security protocols with specific incentives. For example, attackers manage to reap additional rewards by sabotaging computing tasks between two clouds. Unfortunately, most of the existing research works neglect this aspect when discussing the security of protocols. Furthermore, the construction of corrupting two parties is also missing in two-party computation. In this paper, we propose an incentive-driven attacking model where the attacker leverages corruption costs, benefits and possible consequences. We here formalize the utilities used for two-party protocols and the attacker(s), taking into account both corruption costs and attack benefits. Our proposed model can be considered as the extension of the seminal work presented by Groce and Katz (Annual international conference on the theory and applications of cryptographic techniques, Springer, Berlin, pp 81–98, 2012), while making significant contribution in addressing the corruption of two parties in two-party protocols. To the best of our knowledge, this is the first time to model the corruption of both parties in two-party protocols.

The epidemic containment game is a formulation to describe voluntary vaccination behaviors before epidemic spreading. This game relies on the characterization of the susceptible-infected- susceptible (SIS) model in terms of the spectral radius of the network. Existing researches showed that finding the worst Nash Equilibrium (NE) is NP-hard and used a heuristic algorithm called Low Degree (LDG) to estimate the maximum social cost under the worst NE (Max NE cost). By comparing the results of the LDG algorithm and exhaustive search, we found the LDG algorithm cannot estimate Max NE cost well, thus, we proposed a new neighbor information based algorithm to estimate Max NE cost in this paper. Moreover, we discussed Stackelberg strategies in which some nodes are secured first by a leader, then other agents choose their strategies voluntarily. We found the target (TAR) strategy is effective to reduce Max NE cost in a scale- free network when T is large and useless when T is low (T is the ratio of the recovery rate to the transmission rate in the SIS model). Moreover, we found that a lot of nodes with small degrees are secured voluntarily under the TAR strategy when T is low, which leads to high Max NE cost. At last, we proposed a new greedy algorithm to select nodes secured first, which can reduce Max NE cost when T is low.

Traditionally, individual intensities to perform games are always assumed to be fixed in networks (e.g. to depend on the number of their neighbors). However, to increase their own fitness or payoffs, individuals may adjust their intensities in reaction to external environment changes in real scenarios. With this motivation, we have studied this adjustment by considering the average payoff of individual neighbors to be the network environment in a spatial prisoner’s dilemma game. An individual will unilaterally increase (decrease) its intensity to perform games between itself and its neighbors when its payoff is greater than or equal to (lower than) the average payoff of its neighbors. Compared with the normal situation, we find that individual cooperation is significantly facilitated either on the cooperator fraction or the effective cooperation fraction when the environment-induced intensity adjustment is considered, and the value of intensity adjustment per time has a positive influence on the maintenance of cooperation. Evolution snapshots and a formulated typical schematic are used to explain the results. We find that cooperation behaviors are enhanced because of the existence of defectors with lower intensities who are near the boundaries between cooperator and defector clusters. Finally, the promotion is also validated in random networks. We hope that our results may shed light on a greater understanding of the role of individual adaptive behaviors in reaction to network environments in the maintenance of cooperation in societies.

Migration (e.g. between cities and nations) has been shown to be an effective mechanism in facilitating the evolution of cooperation in spatial games. In contingent migration (e.g. success-driven migration), individuals choose the relocation place based on their expected payoffs. In other words, success-driven migration assumes that individuals make decisions about where to migrate strategically rather than randomly. Existing behavioral experiments have shown that human have other-regarding preference. In this paper, we study individuals' cooperation behaviors in the prisoner's dilemma game on a two-dimensional square lattice, where individuals have other-regarding migration preference. We introduce a neighbor-considered migration strategy, which considers both benefits of individuals and their neighbors. During the migration process, an individual always moves to a reachable empty site with the highest fairness payoff, which takes the benefit of all relevant stakeholders (including the particular individual and the neighbors) into consideration. We explore the effect of the different fairness, while considering the individuals when they weigh their own interests and their neighbors' interests. Our simulation results indicate that neighbor-considered migration can effectively promote the level of cooperation by helping cooperative clusters evade the invasion of defectors.

In social networks, individual abilities to establish interactions are always heterogeneous and independent of the number of topological neighbors. We here study the influence of heterogeneous distributions of abilities on the evolution of individual cooperation in the spatial prisoner’s dilemma game. First, we introduced a prisoner’s dilemma game, taking into account individual heterogeneous abilities to establish games, which are determined by the owned game resources. Second, we studied three types of game resource distributions that follow the power-law property. Simulation results show that the heterogeneous distribution of individual game resources can promote cooperation effectively, and the heterogeneous level of resource distributions has a positive influence on the maintenance of cooperation. Extensive analysis shows that cooperators with large resource capacities can foster cooperator clusters around themselves. Furthermore, when the temptation to defect is high, cooperator clusters in which the central pure cooperators have larger game resource capacities are more stable than other cooperator clusters.

Subsidy policies are always used to offer some incentive for individual voluntary vaccination behaviors. The selection of subsidized individuals in proposed policies, such as random subsidy (RAN) and target subsidy (TAR), do not always consider an individual’s history of vaccination behaviors. In this paper, we studied a seasonal influenza-like disease model and proposed two history information-based subsidy policies in which individuals are selected as donees based on vaccination information in the previous seasons: HI-RAN randomly selects individuals who did not voluntarily vaccinate in the previous season, and HI-TAR combines the degree centrality on this basis. Simulations in different networks show that the two proposed subsidy policies both limit the extent of an epidemic outbreak, and the HI-TAR policy is more effective. Moreover, both of our proposed policies are most effective when only one step history information is considered. Through microscopic analysis of the evolution of vaccination behaviors, we found history information-based subsidy policies can enhance the vaccination probability of non-hub nodes. Our work is expected to provide valuable information for vaccination policymaking by considering vaccination history behaviors.

Peer-to-peer (P2P) social networks rely on voluntary resource contributions of peers, understanding and maximizing the effects of resource allocation mechanisms on resource con- tribution of peers have been a focus in such networks. In most of proposed research, the resource sharing dilemma is always modeled by a two-player donor-recipient game in which peers are limited to binary decision (e.g., contribute or not). However, in addition to contributing to multiple recipients simultaneously, a peer also can determine its contribution level in networks. In this paper, we first formulated the resource sharing transaction among a group of peers as a multi-player donor-recipient game with multiple strategies which signify contribution willingness of peers. Then, we studied the influences of two reciprocity based allocation mechanisms in which peers are served based on their direct and total contributions, on the evolution of peers' contribution strategies. Moreover, the influences of some common behaviors of peers (e.g., leave- rejoin and irrational behaviors, slandering behaviors in reporting others' contribution) are also studied. The research is expected to provide valuable information for resource allocation mechanism design in social networks.

In peer-to-peer service networks, autonomous agents gain utilities through getting services from others. However, providing services is so costly that rational agents may prefer to defect rather than to cooperate. In order to provide scalable and robust services in such networks, incentive mechanisms need to be introduced. In this paper, we propose a novel approach RIM (Recommendation Incentive Mechanism) by building a cooperative agents recommendation system. And, in order to investigate the acceptance and performance of the proposed RIM, evolutionary game theory has been used. By studying the evolutionary stable state, we demonstrate the performance of the RIM-based model by both considering two scenarios PRIM/IRIM (Perfect/Imperfect Recommendation Incentive Mechanism). To comprehensively confirm the robustness of our mechanism: (1) OCMP (One Consumer Multi-service Providers) method has been discussed, in which agent could simultaneously request services from k (k>1) different agents; (2) a FS (Four-Strategies) game model has been further introduced; (3) QS (Quantity Sensitivity) scenario has been researched, in which the utility of an agent is sensitive to the amount of received services. By using the Lyapunov stability theory, it is qualitatively proved that non-cooperative agents can be well suppressed in our proposed RIM. Finally, extensive numerical and simulation experiments are conducted to highlight the performance and validate the theoretical properties of our model.

With the development of wireless sensing technologies, numerous sensing applications from the Internet of Things (IoT) are widely used in life and industry. Mobile peer-to-peer (MP2P) system is one of the typical IoT applications, in which peers share their sensing information. Reciprocity-based incentive mechanisms are widely used to encourage cooperation among peers and maintain robustness of MP2P systems. However, the effectiveness of different reciprocity-based mechanisms is difficult to compare theoretically. In this paper, we propose a general evaluating framework to help design and analyze incentive mechanisms for which reciprocal peers can have different reciprocal policies. Using our proposed framework, the evolution dynamics of multiple incentive policies that coexist in MP2P systems can be analyzed. The simulation results show the system robustness and best strategies in various circumstances. In addition, we consider a most common attack model, whitewashing, in MP2P systems and bring in a small entry fee to defeat whitewashers. The results show that this framework can well defend whitewashing and is more suited for real MP2P systems.