WCET (Worst-Case Execution Time) estimation on multicore architecture is particularly challenging mainly due to the complex accesses over cache shared by multiple cores. Existing analysis identifies possible contentions between parallel tasks by leveraging the partial order of the tasks or their program regions. Unfortunately, they overestimate the number of cache misses caused by a remote block access without considering the actual cache state and the number of accesses. This paper reports a new analysis for inter-core cache contention. Based on the order of program regions in a task, we first identify memory references that could be affected if a remote access occurs in a region. Afterwards, a fine-grained contention analysis is constructed that computes the number of cache misses based on the access quantity of local and remote blocks. We demonstrate that the overall inter-core cache interference of a task can be obtained via dynamic programming. Experiments show that compared to existing methods, the proposed analysis reduces inter-core cache interference and WCET estimations by$\mathbf{5 2. 3 1 \%}$and$\mathbf{8. 9 4 \%}$on average, without significantly increasing computation overhead.

In recent years, ridesharing has been widely adopted as an instance of sharing economy. When using ridesharing service, users (i.e., drivers and riders) have to share their private trip information with the service provider, which causes great privacy risks to them. In order to protect user’s privacy, based on private set intersection cardinality (short for PSI-CA), we propose a privacy-preserving and efficient ridesharing scheme (short for PSI-PPER). It enables the service provider to efficiently match riders with appropriate drivers without learning their private trip information. In PSI-PPER, we adopt ‘‘screen and then match” method, which means the service provider first screens each rider’s possible matching drivers, and then performs matching operations on each rider and its possible matching drivers. For matching riders and drivers efficiently, the secure ride-matching computation is converted into private set intersection cardinality and is achieved by a one-party to multi-party PSI-CA protocol, which is designed based on vector oblivious linear evaluation and oblivious key-value stores. We prove the security of the proposed scheme theoretically and evaluate its performance through experiments. The results show that the proposed scheme can achieve practical ride-matching accuracy. In addition, PSI-PPER provides higher security and superior efficiency compared to other secure ride-matching schemes with large number of drivers and large trip length.

The application of deep learning techniques to full waveform inversion (FWI) theory represents a significant research direction. Leveraging the nonlinear representations offered by deep learning and conducting practical FWI are paramount. This article utilizes the classic adjoint method in FWI to compute the gradients of model parameters, employing deep learning to represent model parameters and optimize network training. The focus is on achieving high-precision FWI through multiscale deep learning optimization. Specifically, deep neural networks are used to represent model parameter information and compute gradients of model parameters on high-performance platforms. The gradients of the network parameters are automatically obtained through backpropagation, with deep learning optimization tools updating the network parameters and, consequently, the model parameters. To enhance inversion accuracy, a multiscale learning strategy is introduced, where deep networks optimize the learning of model parameter information at each scale, ensuring effective representation of inversion parameter information across multiple scales. Experimental results demonstrate that deep learning reparameterization methods possess broad-spectrum modeling capabilities. The multiscale deep learning strategy significantly improves inversion accuracy, and the reparameterization method of deep learning shows potential for high-precision modeling under conditions of sparse and noisy observational data. Furthermore, the application of field data underscores the reliability of the proposed method.

This article presents an innovative visual method for measuring material mass online by quantified conveyor belt deformation with deep learning, which offers a noncontact and safe alternative to traditional pressure- and radioactivity-based weighing techniques. The correlation between the belt deformation and the carried material mass is further investigated through finite element simulations. Then, a visual weighing method by belt deformation is proposed, comprising a calibration algorithm to construct a measurement model using a gated recurrent unit-based network, and an online measurement algorithm to calculate material mass with the trained network. Finally, a case study is presented to analyze the effect of different dimension configurations and networks. The results validate that the proposed method attains a notable accuracy and is suitable for high-velocity conveyor environments. The demonstrated benefits signify an advancement in visual perception of materials, enabling a new approach for intelligent operation and monitoring in material handling field.