This paper aims to study people’s trust in the capabilities of social robots in the context of digital transformation. Firstly, the current application status of social robots is studied. Then, the capability trust problem of social robots is studied according to the existing problems and phenomena. Students from a municipal experimental middle school are selected for a questionnaire survey, and the anthropomorphism of social robots is taken as the independent variable. The role of social robots with different anthropomorphic degrees on students’ initial capability trust and the mediating role of attraction perception are studied. A research model is established, and SPSS 26.0 is used to further analyse the data. The results show that among the students with a low degree of an anthropomorphic social robot, the average score of anthropomorphism is 2.52, the average score of attraction perception is 3.29, and the score of capability trust is 3.64, which is the upper-middle level. There are significant differences in the initial capability trust evaluation of social robots among students of different ages (F = 38.13, P = 0.00). When the degree of anthropomorphism of social robots is at different levels, there are significant differences in students’ initial capability trust evaluation (F = 34.25, P = 0.00). It can be seen that the degree of anthropomorphism of social robots has an impact on students’ initial capability trust.

Research on seismic wave propagation in fluid-bearing reservoirs is crucial for unconventional oil and gas exploration. While traditional wave equation-based simulations are well-established, they often struggle to accurately model fluid-solid two-phase media characterized by complex tectonic features, strong discontinuities, and intricate interfacial couplings. This paper presents an advanced approach utilizing two mesoscopic discrete models: the lattice spring model (LSM) and the lattice Boltzmann model (LBM). The LSM simulates solid wavefields via the velocity Verlet algorithm, with optimized spatial distributions of springs and masses. Concurrently, the LBM is employed for fluid wavefield simulation, featuring a precisely designed lattice model and well-defined collision and streaming rules. In particular, a novel momentum exchange strategy incorporating the inverse squared distance method (ISDM) is constructed to enhance wavefield information transfer between the LSM and LBM at fluid-solid boundaries, aiming to improve accuracy and stability. The efficacy of the novel ISDM-LSM-LBM coupling algorithm is validated through simulations of layered models, fluid-filled cavity model, and river filling models. Compared to the original LSM-LBM algorithm, it exhibits enhanced computational accuracy and stability, albeit with a modest increase in computational burden and memory consumption. Comparative analysis with finite difference method (FDM) results reveals that the ISDM-LSM-LBM algorithm accurately captures wavefield characteristics. Although slight differences are observed in the depiction of reflected, transmitted, and converted waves at internal boundaries—attributable to the distinct fluid-solid interface treatments of each method—the new algorithm’s flexibility in boundary coupling and consideration of additional interaction forces offer significant improvements in accuracy and stability. This innovative simulation technique, independent of traditional wave equations, significantly enhances the precision and adaptability of wavefield calculations in fluid-solid media, providing substantial benefits for oil and gas geophysical exploration.

When the structure of the media is complex or there are strong interfaces, the simulated seismic waves obtained by the traditional forward modeling methods are often difficult to fit the real wavefields. To get more accurately simulated waves, in this article, we utilize the widely used 3-D lattice Boltzmann model (LBM) in fluid mechanics as a new tool for seismic wavefield simulation. In order to establish a general framework for the LBM in the 3-D case, first, we introduce an improved LBM scheme in 3-D form considering the reflection and transmission effects. Then, we carry out a systematic dispersion analysis of the LBM with different lattices by analogizing the dispersion derivation of the finite-difference method (FDM) and comparing the results of the LBM with those of the FDM. Finally, we explore the stability conditions and present the treatment of initial and boundary conditions. To verify the feasibility of this 3-D-LBM strategy, we perform 3-D wavefield simulation tests on a homogeneous model, a cavity model, and a five-layer model, respectively. By comparing the results obtained by LBM and FDM, we conclude that the accuracy of this improved LBM scheme is acceptable. This 3-D-LBM modeling scheme provides a new option for seismic wavefield simulation for complex media due to its flexibility of boundary processing and efficient parallelism capability.

Full-waveform inversion (FWI), as a high-precision model building method, plays an invaluable role in seismic exploration. The accuracy of conventional FWI is universally reduced by the cycle skipping, which can be improved by the optimal transport distance (OTD) objective function. However, the OTD-based FWI cannot converge to meaningful results with an inaccurately estimated source wavelet. To solve this dilemma, we construct a novel convolutional Wasserstein distance (CW) objective function by applying the OTD objective function to convolved seismograms. Before the standard non-negative and normalization preprocessing of OTD, we first convolve the observed data with a reference trace selected from simulated seismograms and convolve the simulated data with a trace selected from the observed data. The both convolved data sets are naturally regarded as with an identical source, so the data difference caused by the inaccurately estimated source wavelet is eliminated. The adjoint source corresponding to the new objective function is derived. The velocity model can be updated by using a quasi-Newton method according to the gradient of the objective function generated by the adjoint-state method. We investigate the effectiveness of our objective function by one-dimensional signals and several FWI examples. Furthermore, this new objective function still delivers an excellent performance in releasing the local minimum problem and resisting noise when the wavelet used in FWI is inaccurate.

Lattice spring model (LSM) is a novel method to simulate seismic wave propagation from a micromechanical perspective, which describes the elastic dynamics in complex media comprehensively and delineates the dynamic characteristics of the wavefield. However, it remains a great challenge to simulate wavefields at irregular interfaces with high accuracy. This work presents an adaptive location LSM (ALLSM) method to substantially improve the accuracy of simulated wavefield in a solid model. First, we design an algorithm to pick up the intersection coordinates of the internal interfaces with regular mesh. We obtain the intersection coordinates by solving the defined interface functions with different kinds of spring functions in the LSM. Then, we calculate the weights of springs with different elastic coefficients in the whole grid by the intersection coordinates. Finally, we obtain the real spring elastic coefficients in the presence of irregular interfaces by the arithmetic weighted average method, and develop the corresponding interface position ALLSM to simulate the wavefield propagation mechanism, in order to substantially improve the accuracy of simulation. Numerical tests show that the improved ALLSM is more accurate than conventional LSM and second-order finite difference method (FDM). The accuracy of simulated wavefields is significantly improved about 20%. The proposed method offers a new tool for wavefields simulation in complex media.

Reverse-time migration (RTM) has the particular capacity for complex geological structure imaging. However, massive storage and high computational costs caused by conventional cross-correlation imaging conditions restrict the large-scale application of RTM. The excitation amplitude imaging condition (EAIC) has the cheapest imaging cost, but inadequate wavefield information causes less tolerance for noise. To alleviate these limitations, we introduce a local Nyquist cross-correlation imaging condition, which serves as a transition between the conventional cross-correlation imaging conditions and the EAIC. Instead of using the full wavefields or only the excitation amplitude to construct imaging, the local cross-correlation imaging condition (LCIC) utilizes the wavefields around the corresponding time of the maximum amplitude at each grid during the source wavefield simulation. Moreover, considering the possible oversampling situations in the LCIC, the Nyquist sampling theorem is flexibly embedded into the local cross-correlation scheme to further reduce the storage costs and improve the efficiency of RTM. Compared with the cross-correlation imaging conditions, the proposed strategy can obtain the similar results and reduce the storage requirement and time costs significantly. In the meantime, it maintains better adaptability to the complex imaging environments than the EAIC. Migration tests of synthetic and field datasets demonstrate that the local Nyquist cross-correlation scheme features good feasibility, efficiency, and practicability in RTM. As a consequence, the proposed local Nyquist cross-correlation imaging condition can effectively save storage and time costs and provide a reliable migrated image even from noisy observed data.

As promising alternatives in building future main memory systems, emerging non-volatile memory (NVM) technologies can increase memory capacity in a cost-effective and power-efficient way. However, NVM is facing security threats due to its limited write endurance: a malicious adversary can wear out the cells and cause the NVM system to fail quickly. To address this issue, several wear-leveling schemes have been proposed to evenly distribute write traffic in a security-aware manner. In this study, we present a new type of timing attack, remapping timing attack (RTA), based on information leakage from the remapping latency difference in NVM. Our analysis and experimental results show that RTA can cause three of the latest wear-leveling schemes (i.e., region-based start-gap, security refresh, and multi-way wear leveling) to lose their effectiveness in several days (even minutes), causing failure of NVM. To defend against such an attack, we further propose a novel wear-leveling scheme called the ‘security region-based start-gap (security RBSG)’, which is a two-stage strategy using a dynamic Feistel network to enhance the simple start-gap wear leveling with level-adjustable security assurance. The theoretical analysis and evaluation results show that the proposed security RBSG not only performs well when facing traditional malicious attacks, but also better defends against RTA.

NAND flash-based Solid State Drives (SSDs) employ the Flash Translation Layer (FTL) to perform logical-to-physical address translation. Modern page-level FTLs selectively cache the address mappings in the limited SRAM while storing the mapping table in flash pages (called translation pages). However, many extra accesses to the translation pages are required for address translation, which decreases the performance and lifetime of an SSD. In this paper, we propose a Log-aware Synergized scheme for page-level FTL to reduce the extra overheads, called LSFTL. The contribution of LSFTL consists of two key elements: (i) By exploiting the partial programmability of SLC flash, in-place logging decreases garbage collection overhead via reserving a small portion of each translation page as a logging area to hold multiple updates to the entries of that translation page. (ii) Log-aware flush back reduces the number of translation page updates by evicting multiple dirty cache lines that share the same translation page in a single transaction. Extensive experimental results of trace-driven simulations show that LSFTL decreases the system response time by 39.40% on average, and up to 58.35%, and reduces the block erase count by 37.55% on average, and up to 39.99%, compared to the well-known DFTL.

As an emerging memory technology to build the future main memory systems, Phase Change Memory (PCM) can increase memory capacity in a cost-effective and power-efficient way. However, PCM is facing security threats for its limited write endurance: a malicious adversary could wear out the cells and cause the whole PCM system to fail within a short period of time. To address this issue, several wear-leveling schemes have been proposed to evenly distribute write traffic in a security-aware manner. In this work, we present a new type of timing attacknamed Remapping Timing Attack (RTA), based on the asymmetry in write time of PCM. Our analysis and experimental results show that the new revealed RTA can make two state-of-the-art wear-leveling schemes (Region Based Start-Gap and Security Refresh) lose effectiveness, failing PCM with these two techniques in several days (even minutes). In order to defend such attack, we further propose a novel wear-leveling scheme called Security Region Based Start-Gap (Security RBSG), which employs a two-stage strategy and uses a dynamic Feistel Network to enhance the simple Start-Gap wear leveling with level-adjustable security assurance. The theoretical analysis and evaluation results show that the proposed Security RBSG is the most robust wear-leveling methodology so far, which not only better defends the new RTA, but also performs well on the traditional malicious attacks, i.e., Repeated Address Attack and Birthday Paradox Attack.

Sampled microarchitectural simulation of single-threaded applications is mature technology for over a decade now. Sampling multithreaded applications, on the other hand, is much more complicated. Not until very recently have researchers proposed solutions for sampled simulation of multithreaded applications. Time-Based Sampling (TBS) samples multithreaded application execution based on time—not instructions as is typically done for single-threaded applications—yielding estimates for a multithreaded application’s execution time. In this article, we revisit and analyze previously proposed TBS approaches (periodic and cantor fractal based sampling), and we obtain a number of novel and surprising insights, such as (i) accurately estimating fast-forwarding IPC , that is, performance in-between sampling units, is more important than accurately estimating sample IPC , that is, performance within the sampling units; (ii) fast-forwarding IPC estimation accuracy is determined by both the sampling unit distribution and how to use the sampling units to predict fast-forwarding IPC; and (iii) cantor sampling is more accurate at small sampling unit sizes, whereas periodic is more accurate at large sampling unit sizes. These insights lead to the development of Two-level Hybrid Sampling (THS) , a novel sampling methodology for multithreaded applications that combines periodic sampling’s accuracy at large time scales (i.e., uniformly selecting coarse-grain sampling units across the entire program execution) with cantor sampling’s accuracy at small time scales (i.e., the ability to accurately predict fast-forwarding IPC in-between small sampling units). The clustered occurrence of small sampling units under cantor sampling also enables shortened warmup and thus enhanced simulation speed. Overall, THS achieves an average absolute execution time prediction error of 4% while yielding an average simulation speedup of 40 × compared to detailed simulation, which is both more accurate and faster than the current state-of-the-art. Case studies illustrate THS’ ability to accurately predict relative performance differences across the design space.

Warm up is a crucial issue in sampled micro architectural simulation to avoid performance bias by constructing accurate states for micro-architectural structures before each sampling unit. Not until very recently have researchers proposed Time-Based Sampling (TBS) for the sampled simulation of multi-threaded applications. However, warm up in TBS is challenging and complicated, because (i) full functional warm up in TBS causes very high overhead, limiting overall simulation speed, (ii) traditional adaptive functional warm up for sampling single-threaded applications cannot be readily applied to TBS, and (iii) check pointing is inflexible (even invalid) due to the huge storage requirements and the variations across different runs for multi-threaded applications. In this work, we propose Shorter On-Line (SOL) warm up, which employs a two-stage strategy, using 'prime' warm up in the first stage, and an extended 'No-State-Loss (NSL)' method in the second stage. SOL is a single-pass, on-line warm up technique that addresses the warm up challenges posed in TBS in parallel simulators. SOL is highly accurate and efficient, providing a good trade-off between simulation accuracy and speed, and is easily deployed to different TBS techniques. For the PARSEC benchmarks on a simulated 8-core system, two state-of-the-art TBS techniques with SOL warm up provide a 7.2× and 37× simulation speedup over detailed simulation, respectively, compared to 3.1× and 4.5× under full warm up. SOL sacrifices only 0.3% in absolute execution time prediction accuracy on average.

Computer architects rely heavily on microarchitecture simulation to evaluate design alternatives. Unfortunately, cycle-accurate simulation is extremely slow, being at least 4 to 6 orders of magnitude slower than real hardware. This longstanding problem is further exacerbated in the multi-/many-core era, because single-threaded simulation performance has not improved much, while the design space has expanded substantially. Parallel simulation is a promising approach, yet does not completely solve the simulation challenge. Furthermore, existing sampling techniques, which are widely used for single-threaded applications, do not readily apply to multithreaded applications as thread interaction and synchronization must now be taken into account. This work presents PCantorSim , a novel Cantor set (a classic fractal)--based sampling scheme to accelerate parallel simulation of multithreaded applications. Through the use of the proposed methodology, only less than 5% of an application's execution time is simulated in detail. We have implemented our approach in Sniper (a parallel multicore simulator) and evaluated it by running the PARSEC benchmarks on a simulated 8-core system. The results show that PCantorSim increases simulation speed over detailed parallel simulation by a factor of 20×, on average, with an average absolute execution time prediction error of 5.3%.