With the development of cloud computing, increasing attention has shifted from traditional manufacturing toward cloud manufacturing. The distinguishing feature of cloud manufacturing is that the resource designated for a given manufacturing service is always massive, complex, and heterogeneous. Cloud manufacturing also involves a high degree of user participation and user diversity. This article presents a method of resource selection based on service evaluation, which combines predictive evaluations and recommended evaluations. Predictive evaluations are based on the user’s historical evaluations of a given service, and these evaluations may be weighted differently depending on the time when the service occurred. Recommended evaluations are given by recommenders who are selected by a two-step process. These evaluations are weighted according to their similarities and objectivities. The results of numerical experiments show that the proposed method performs better than previous methods.

Crowd counting is an important research topic in the field of computer vision. The multi-column convolution neural network (MCNN) has been used in this field and achieved competitive performance. However, when the crowd distribution is uneven, the accuracy of crowd counting based on the MCNN still needs to be improved. In order to adapt to uneven crowd distributions, crowd global density feature is taken into account in this paper. The global density features are extracted and added to the MCNN through the cascaded learning method. Because some detailed features during down-sampling process will be lost in the MCNN and it will affect the accuracy of the density map, an improved MCNN structure is proposed. In this paper, the max pooling is replaced by max-ave pooling to keep more detailed features and the deconvolutional layers are added to restore the lost details in down-sampling process. The experimental results in the UCF_CC_50 dataset and ShanghaiTech dataset show that the proposed method has higher accuracy and stability.

In ramp metering methods, the ALINEA algorithm is a very effective way and has been applied widely. But the critical occupancy in ALINEA algorithm is often difficult to obtain and not particularly accurate. It will greatly affect the performance of ALINEA algorithm. In this paper, an improved ALINEA algorithm, named CS-ALINEA, is proposed. In this method, the traffic flow is used to replace the occupancy as the control parameter and the control rate can be selected according to the congestion status reclassified adaptively. In the existing ramp control methods, to guarantee the traffic efficiency of mainstream, the impact of ramp overflow on ground road traffic is often ignored. In order to resolve this issue, the segmented control method is adopted in this paper. When the ramp queuing length exceeds the critical queue length, the signal timing scheme is adjusted by selecting the control rate to avoid the ramp overflow. The SUMO simulation platform is used to simulate the ramp control and test the CSALINEA algorithm. The experimental results show that the proposed method can optimize the ramp queuing length and reduce waiting time of vehicles while the efficiency of urban freeway can be guaranteed.

Cloud manufacturing (CMfg) is a new service‐oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on‐demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi‐Batch Subtasks Parallel‐Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE‐CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP‐hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.

Sentiment-controlled text generation aims to generate texts according to the given sentiment. However, most of the existing studies focus only on the document- or sentence-level sentiment control, leaving a gap for finer-grained control over the content of generated results. Fine-grained control allows a generated review to express different opinions toward multiple aspects. Some previous works attempted to generate reviews conditioned on aspect-level sentiments, but they usually suffer from low adaptability and the lack of an annotated dataset. To alleviate these problems, we propose a novel pre-trained extended generative model that can dynamically refer to the prompt sentiment, together with an auxiliary classifier that extracts the fine-grained sentiments from the unannotated sentences, thus we conducted training on both annotated and unannotated datasets. We also propose a query-hint mechanism to further guide the generation process toward the aspect-level sentiments at every time step. Experimental results from real-world datasets demonstrated that our model has excellent adaptability in generating aspect-level sentiment-controllable review texts with high sentiment coverage and stable quality since, on both datasets, our model steadily outperforms other baseline models in the metrics of BLEU-4, METETOR, and ROUGE-L etc. The limitation of this work is that we only focus on fine-grained sentiments that are explicitly expressed. Moreover, the implicitly expressed fine-grained sentiment-controllable text generation will be an important puzzle for future work.