Spatial crowdsourcing (SC) can use the moving workers to achieve location-based tasks. It has been widely used in takeout, data labeling, organizing activities, security and artificial intelligence. Currently, many manufacturers wish to explore SC in maintenance business, as SC can reduce maintenance time, reduce labor costs, and improve customer satisfaction. In maintenance business scenarios, there are two kinds of cooperated participators. One is freedom workers who are sensitive to distance, and the other is employed by manufacturers who are insensitive to distance. Therefore, some methods of SC with one type worker are challenging to apply to maintenance business scenarios directly. To match this scenario, we model the maintenance scenario and prove that this scenario is an NP-hard problem; then, both greedy and Nash equilibrium methods are proposed to complete the tasks for making a high total reward. The greedy algorithm (GA) first assigns the nearest available workers to each task, and the employee will try to join the task to help GM to complete more tasks, on the condition that the task cannot be completed and reaches a particular proportion. The Nash equilibrium algorithm (NA) is used to find a Nash equilibrium for all the workers and employees. The experiments demonstrate the efficiency and effectiveness of the synthetic data set of gMission in small and large data sets. The finished task number of NA is about 5% more than that of GA, and the reward of NA is about 10% more than that of GA.

The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy rate in downstream tasks is increased by 0.65% on average after using BERTBooster on CAGBERT.

Aiming at the problems of secondary pollution and resource waste caused by inaccurate input of coal and ammonia in coal-fired power plant, an optimal controlling method of combustion and denitration coordinated operation based on Deep Deterministic Policy Gradient (DDPG) is proposed in this paper. First, the environmental model is constructed by the Stacking algorithm to predict the NOx emission concentration of the combustion and denitration system, which provides environmental state feedback for the optimal controlling model. Second, the optimization controlling model is constructed based on the DDPG algorithm within the standard limitation of denitration efficiency and NOx emission concentration. This model takes the minimization of comprehensive cost as its optimization objective to realize the optimal control of controllable variables in the cooperative operation process of combustion and denitration. The experimental results of real operational data from 1000 MW boiler unit in a power plant locating in south China show that the optimization results of coordinated operation for the combustion and denitration system are better than single-stage optimization results. In addition, the total cost is reduced by 1%–3% on average compared with before optimization.

This erratum replaces the corresponding author Liang Tiancai with Ai Shan. In the article cited above, the authors wish to change the corresponding author from Liang Tiancai to Ai Shan as shown below.1 Shan Ai School of Computer Science and Cyberspace Security, Hainan University, Hainan, China Email address: [email protected] ORCID ID: 0000-0002-1784-0220

Machine unlearning is the process through which a deployed machine learning model is enforced to forget about some of its training data items. It normally generates two machine learning models, the original model and the unlearned model, indicating training results before and after data items are deleted. However, recent studies find that machine unlearning is vulnerable to membership inference attacks—as the directivity of training and nontraining data (i.e., data items in the training set have high posterior probabilities), the attackers can utilize this property to infer whether an item has been used for original model training. Nevertheless, such attacks are incapable in label-only settings, in which the attackers are infeasible to get the posteriors. In this paper, we propose a new label-only membership inference attack scheme targeted at machine unlearning to eliminate the dependence on posteriors. Our heuristic is that injected turbulence on candidate samples will present different behaviors for training and nontraining data. Thus, in our scheme, the attacker iteratively query on the original/unlearned models and inject turbulence to change their predicting labels; it determines whether an item is having-been-delated by observing the disturbance amplitude. Extensive experiments (i.e., on MNIST, CIFAR10, CIFAR100, and STL10 data sets) show that our method achieves high inference accuracy (measured by AUC) in label-only settings, for example, AUC = 0.96 for MNIST data set. Besides, we analyze the existing countermeasures in mitigating inference attacks and find that our scheme can bypass most of them.

Aiming at the problems of complex structure, high components coupling, and difficultly monitoring of the whole health status with the industrial robot, a metric learning-based whole health indicator model is proposed. First, according to the more obvious degradation characteristics of industrial robots during accelerated operation, the accelerated signal is segmented and then the time-domain features are extracted. Second, the long-term and short-term memory (LSTM) network combined with the multihead attention is used to construct the network model, and the metric learning method is adopted to learn the similarity measurement method of the industrial robot monitoring data. Finally, the similarity measure method got from metric learning is used to construct the whole health indicator, which describes the whole degradation trend of the industrial robot. The experiments are based on the real accelerated aging data set from industrial robots. The results show that the proposed model can effectively construct the whole health indicator for industrial robots. The average trend of the proposed model reaches 0.9769. The average monotonicity reaches 0.5666, which is 0.1748, 0.1577, and 0.1492 higher than the similarity measurement method based on Euclidean distance, Markov distance, and LSTM.

Since the new crown epidemic, mask-wearing has become a new normal in people's work and life. The inspection mechanism for mask-wearing at the entrance and exit of public places is seriously insufficient. The phenomenon of “pick-up on entry” has led to the severe formalization of mask-wearing inspection. Manual detection of mask-wearing in an open and dynamic crowded environment is unrealistic, which is not only time-consuming and labor-intensive but also cannot achieve early warning throughout the entire process. In response to this problem, this paper proposes a real-time recognition and early warning method for mask-wearing in an open, dynamic, complex environment based on improved YOLOv5 R6.1. First, replacing the first Conv structure of the backbone network in the YOLOv5 R6.1 model with an improved Stem structure to minimize the computational overhead while improving the performance. Then by normalizing the data, the random erasure data expansion technique is used to enhance the antiocclusion robustness of the algorithm. Finally, according to the mask-wearing specification in the training data set, optimizing and adjusting the anchor box parameters of the YOLOv5 R6.1 model to improve the model's ability to recognize small targets. The experiments are based on open data sets, and the results show that the mean precision (mAP), precision, and recall of this method reach 92.9%, 94.1%, and 88.5% on average, and the average frames per second (FPS) reaches 117. Moreover, the mAP and FPS are improved by an average of 6.5% and 474% compared with algorithms based on RetinaNet, Attention-Retina, Single Shot multibox Detector, Fast-RCNN, YOLOv4, and YOLOv5.

Networks are ubiquitous in the real world such as social networks and communication networks, and anomaly detection on networks aims at finding nodes whose structural or attributed patterns deviate significantly from the majority of reference nodes. However, most of the traditional anomaly detection methods neglect the relation structure information among data points and therefore cannot effectively generalize to the graph structure data. In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks. Specifically, Dual-SVDAE consists of a structure autoencoder and an attribute autoencoder to learn the latent representation of the node in the structure space and attribute space, respectively. Then, a dual-hypersphere learning mechanism is imposed on them to learn two hyperspheres of normal nodes from the structure and attribute perspectives, respectively. Moreover, to achieve joint learning between the structure and attribute of the network, we fuse the structure embedding and attribute embedding as the final input of the feature decoder to generate the node attribute. Finally, abnormal nodes can be detected by measuring the distance of nodes to the learned center of each hypersphere in the latent structure space and attribute space, respectively. Extensive experiments on the real-world attributed networks show that Dual-SVDAE consistently outperforms the state-of-the-arts, which demonstrates the effectiveness of the proposed method.

Security communication and information sharing among mobile devices are important application technologies of the intelligent information system (IIS). Because IIS is vulnerable to attacks, so the security of information sharing among mobile devices is seriously threatened. Thence, it is necessary to establish a secure channel for communication among mobile devices of IIS over an opening network. Group key agreement (GKA) can establish a secure channel among mobile devices of IIS by encryption technology. Due to the resource-constraints of mobile devices, such as weak computing power, small storage capacity, and limited communication range. To address these issues, an asymmetric GKA protocol among terminals of IIS for mobile edge computing (GKA–IIS–MEC) network is proposed in this paper. Adopted asymmetric GKA to achieve the group secure communication mechanisms that message sender unfettered in this protocol; the protocol also uses edge computing environment to migrate the computation and communication loads of mobile devices of IIS to edge nodes, thereby ensuring that mobile devices have lightweight computation and communication loads; and the members participating in the GKA can verify whether the group session keys they calculated are correctness. Under the hardness assumption of bilinear inverse Diffie–Hellman problem, the proposed protocol is proven that it can resist negative attacks. After evaluating the performance of the protocol, GKA–IIS–MEC has higher efficiency than the referred works in terms of time cost, communication consumption, and computation consumption.

When making regression predictions, the traditional random forest (RF) algorithm can only make predictions within the training set, which can easily lead to overfitting when modeling data have some specific noise. To solve the problem of over-fitting, an improved RF method is proposed in this paper for wind pressure prediction. With the aim to verify the prediction performance of the improved RF algorithm, this paper predicts the wind pressure coefficients of a high-rise building model without wind pressure measurement points. The results show that the improved RF can achieve good results in predicting the mean and fluctuating wind pressure coefficients of high-rise buildings, and its relative error for each measurement point is basically controlled at 5%, which is acceptable in engineering terms. Further applications show that this improved RF can be used for wind pressure distribution prediction in other large-span building type wind tunnel tests.

Android platform has been the target of attackers due to its openness and increasing popularity. Android malware has explosively increased in recent years, which poses serious threats to Android security. Thus proposing efficient Android malware detection methods is curial in defeating malware. Various features extracted from static or dynamic analysis using machine learning have played an important role in malware detection recently. However, existing code obfuscation, code encryption, and dynamic code loading techniques can be employed to hinder systems that single based on static analysis, purely dynamic analysis systems cannot detect all potential code execution paths. To address these issues, we propose CoDroid, a sequence-based hybrid Android malware detection method, which utilizes the sequences of static opcode and dynamic system call. We treat one sequence as a sentence in the natural language processing and construct a CNN–BiLSTM–Attention classifier which consists of Convolutional Neural Networks (CNNs), the Bidirectional Long Short-Term Memory (BiLSTM) with an attention language model. We extensively evaluate CoDroid under a real-world data set and perform comprehensive analysis against other existing related detection methods. The evaluations show the effectiveness and flexibility of CoDroid across a variety of experimental settings.