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Aiming at the distributed unrelated parallel machine scheduling problem with machining constraints, this paper proposed an extended empire competition algorithm to solve the problem, which took the total transportation cost, evaluation function for simultaneous shutdown of parallel machines among factories and equilibrium evaluation function for average switching frequency of workpiece types among factories as optimization objectives. Based on the original empire competition algorithm, this algorithm added an initial factory-workpiece sequence group suitable for factory assignment. According to the shortcoming of the traditional empire competition algorithm that it was easy to fall into the local optimum, this paper divided the inferior sequence assimilation into the external assimilation mechanism and the internal assimilation mechanism. The extended empire competition algorithm combined local and global search methods to realize the intelligent search behavior. This paper used the partial matching crossover and single point mutation to update the factory-workpiece sequence group and ensure the diversity of the factory-workpiece sequence. Finally, this paper designed three different scales of 12 examples and verified the effectiveness of the proposed algorithm through simulation experiments. At the same time, this paper verified the advantages of the proposed algorithm in solving the distributed multi-objective unrelated parallel machine scheduling problem by comparing the research results in related fields. [ABSTRACT FROM AUTHOR]

To solve the problems of low recognition accuracy and poor generalization performance of existing face anti-spoofing models in different application scenarios, this paper adopted the idea of disentangled representation learning and proposed a face anti-spoofing method based on disentangled representation learning. This method adopted U-Net architecture and ResNet18 as the encoder-decoder. In the first stage of training, it inputted real samples so that the encoder only learned information related to real samples. In the second stage, this paper built an adversarial learning network, inputted samples without labels, feature fusion of the pre-trained encoder output and the new encoder output, reconstructed the image by the decoder, and performed adversarial training with the original image in the discriminator to achieve feature decoupling. Compared with some classic face anti-spoofing methods, the model paper achieved better detection performance. The lowest detection error rate in several experiments on the OULU-NPU data set is only 0.8%, which is better than classic detection methods such as STDN. The face anti-spoofing method used staged training to enable the model to obtain a more discriminative feature representation than the end-to-end model in adversarial training. It adopted a multi-classification strategy in the deception feature map output stage to reduce the impact of different image noises on classification results, and experiments on public data sets verified the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Most of the existing MAPF methods based on communication learning have poor scalability or aggregate too much redundant information, resulting in inefficient communication. To solve these problems, this paper proposed disruptor identifiable communication (DIC), which learned concise communication excluding non-disruptors by judging whether the agent in the center of the field of view would change its decision-making due to the presence of neighbors, and successfully filtered out redundant information. At the same time, this paper further instantiated DIC and developed a new highly scalable distributed MAPF solver: disruptor identifiable communication based on reinforcement and imitation learning algorithm (DICRIA). Firstly, the disruptor discriminator and the policy output layer of DICRIA identified the disruptor. Secondly, the algorithm updated the information of the disruptor and the communication wish sender in two rounds of communication respectively. Finally, DICRIA output the final policy according to the coding results of each module. Experimental results show that DICRIA S performance is better than other similar solvers in almost all environment settings, and the algorithm increases the success rate by 5.2% on average compared to the baseline solver. Especially in dense problem instances with large-size maps, the algorithm even increases the success rate of DICRIA by 44.5% compared to the baseline solver. [ABSTRACT FROM AUTHOR]

The combination processing of molds and electronic product joint inspection introduce the mandatory concurrent operations on the same machine (MCDSM), which breaks the constraint that the same machine cannot process different jobs at the same time in the job shop scheduling (JSP) To solve this type of job shop scheduling problem, this paper proposed an adaptive hybrid initialization genetic algorithm (AHIGA). Firstly, this paper defined the problem as a generalized JSP considering mandatory concurrent operations on the same machine (GJSPMCO) problem, and established an optimization model using mixed integer programming with the objective of minimizing the maximum completion time. Next, it designed corresponding encoding and decoding techniques to support the representation and resolution of feasible scheduling plans within the constraints of MCOSM. Meanwhile, it established a population hybrid initialization method to generate high-quality feasible solutions. This paper also designed novel crossover and mutation operation methods to ensure the feasibility of newly generated solutions under the constraint of MCOSM. Additionally, it constructed an adaptive crossover and mutation operators to enable the adaptive updating of offspring, thereby enhancing the algorithms global search capability. Finally, it constructed 40 test cases based on JSP benchmark, and used these test cases and an example of joint inspection of electronic products for experiments. The results show that the proposed model and algorithm can effectively solve GJSPMCO problem, and the improvement strategies can enhance the quality of solutions, demonstrating the feasibility of the model and the superiority of AHIGA. [ABSTRACT FROM AUTHOR]

Periodic high utility sequential pattern mining (PHUSPM) has attracted much attention because it can find more practical regular patterns in time series. However, existing PHUSPM algorithms struggle to effectively handle incremental updates and overlook the downward closure property and complexity of the algorithm in large-scale data. To solve this problem, this paper proposed an IncPUS-Miner algorithm, which effectively realized the incremental mining of periodic high-utility sequential patterns (PHUSPs). IncPUS-Miner introduced a novel data structure called pu-tree. Each tree node corresponded to an updated utility list(UUL) to store the auxiliary information of the corresponding sequence. When incremental data was added, this structure allowed flexible updates to project information, thereby enhancing the dynamic adaptability and scalability of the algorithm. In addition, this paper proposed two new upper bounds of sequence utility, PUB and EUB, and two corresponding pruning strategies, which effectively reduced the computational burden. The experimental results show that the IncPUS-Miner algorithm effectively realizes the incremental mining of PHUSPs on real data, and shows superior performance compared with other algorithms. [ABSTRACT FROM AUTHOR]

Threshold private set intersection (TPSI) is a special case of secure multi-party computation, which is widely used in many fields such as machine learning, ride-sharing, and fingerprint recognition. However, currently existing solutions are all based on algorithms with high computational complexity and are only implemented under a semi-honest model, resulting in high computational overhead and inability to resist attacks by malicious adversaries. In order to solve the above problems, this paper first proposed a vector oblivious match test(VOMT) protocol and designed an efficient semi-honest TPSI protocol based on VOMT and cuckoo hashing. In addition, it combined VOMT and symmetric key encryption schemes to construct the vector oblivious decryption matching test(VODMT) protocol and designed a TPSI protocol based on VODMT and oblivious pseudorandom functions that could resist malicious adversaries. Subsequently, it proved the security of the protocol under the semihonest model and the malicious model respectively, and analyzed that the computational complexity and communication complexity of the two protocols were linear. When the set size is 4 096, the online running time of the two proposed protocols is 0.81 s and 1.81 s respectively, while the previous work required 5 627 s, so both protocols are efficient. [ABSTRACT FROM AUTHOR]

Aiming at the problem of difficulty in allocating channels efficiently for secondary users (SU) in complex and variable cognitive radio systems, this paper proposed a high throughput channel allocation protocol, TKMA protocol. The protocol constructed an utility matrix of user and channel based on primary user (PU) activities, SU real-time service demands, and channel conditions, etc., allocated channels with the objective of maximizing the total utility value of the SU system under the premise of guaranteeing the communication quality of the PUs, and solved the problem by using the improved Kuhn-Munkras algorithm and polling scheduling. To evaluate the performance of the protocol, it established a universal multi-user multi-channel cognitive radio system model, described the packet transmission process using queuing theory, and derived the performance indicators of SU through Markov steady-state solution. The experimental results show that the use of TKMA protocol yields better results in terms of total SU system throughput, average delay, average queue length, rejection rate and other metrics compared with the previously proposed simplification algorithm and the traditional equitable random assignment protocol, which proves the effectiveness of the proposed protocol and system model. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of node connection and energy limitation in wireless sensor networks, this paper proposed a clustering routing algorithm based on AHC (HACCRA) to prolong the network lifetime. The algorithm firstly applied the aggregated hierarchical clustering to cluster network nodes, and then defined appropriate decision objective functions for cluster head (CH) selection, cluster formation, and path construction respectively, it used the energy threshold, proposed the distance threshold, and took priority of the one-to-one connection between CHs during the process of nodes' data transmission, effectively solving the unconnected problem of clustering and routing in algorithms. The simulation results show that compared with JCR, ICR, and DCK-LEACH, HACCRA can better achieve nodes' balanced energy-consumption, and ensure effective connection of data transmission in the network, so as to prolong the network lifetime. [ABSTRACT FROM AUTHOR]

The conventional cloud-end MCS system currently faces problems of excessive load, leading to a significant increase in delay and energy consumption during the data aggregation process, inevitably causing a decrease in data aggregation efficiency. To tackle this issue, this paper proposed a cloud-edge-end MCS computation offloading algorithm based on APDQN. Firstly, it established a utility function considering the balanced optimization of delay and energy consumption, with the maximization of system utility as an optimized goal. Secondly, improving the P-DQN algorithm, it proposed a computational offloading algorithm AP-DQN for combining resource allocation. This algorithm, leveraging the advantages of MCS, designated idle users as one of the offloading devices. Finally, the problem was solved using the proposed method. Experimental results show that, compared to existing algorithms, the proposed method significantly improves data aggregation efficiency and maintains excellent system stability. [ABSTRACT FROM AUTHOR]

In the deep reinforcement learning method, the intrinsic curiosity model (ICM) guides the agent to obtain the opportunity to learn unknown strategies in the sparse reward environment, but the curiosity reward is a state difference value, which will make the agent pay too much attention to the exploration of new states, then could be the problem of blind exploration arises. To solve the above problem, this paper proposed an intrinsic curiosity model algorithm based on knowledge distillation (KD-ICM). Firstly, it introduced the method of knowledge distillation to make the agent acquire more abundant environmental information and strategy knowledge in a short time and accelerate the learning process. Secondly, by pre-training teachers' neural network model to guide the forward network to obtain a forward network model with higher accuracy and performance, reduced the blind exploration of agents. It designed two different simulation experiments on the Unity simulation platform for comparison. The experiments show that in the complex simulation task environment, the average reward of KDICM algorithm is 136% higher than that of ICM, and the optimal action probability is 13.47% higher than that of ICM. Both the exploration performance of the agent and the exploration quality can be improved, and it verifies the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The single domain generalization (SDG) based on meta-learning has emerged as an effective technique for solving the domain-shift problem. However, the inconsistent semantic information between source and augmented domains and difficult separation of domain-invariant features from domain-related features make SDG model hard to achieve great generalization. To address the above problems, this paper proposed a novel meta-learning method based on domain enhancement and feature alignment (MetaDefa) to improve the model generalization performance. This method utilized background replacement and visual damage techniques to generate diverse and effective augmented images for each image, ensuring the consistency of semantic information between the source domain and the enhanced domains. The multi-channel feature alignment module fully mines image information by focusing on similar target regions between the source and enhanced domains feature spaces and suppressing feature representations of non-target areas, thereby effectively extracting sufficient transferable knowledge. Through experimental evaluation, MetaDefa achieved 88.87%, 73.06% and 57.06% accuracy on office-Caltech-10, office31 and PACS datasets, respectively. The results show that the MetaDefa method successfully achieves semantic consistency between the source and augmented images and adequate extraction of domain-invariant features, which significantly improves the generalization performance of single domain generalization models. [ABSTRACT FROM AUTHOR]