Sales Model Selection for Second-hand vehicle E-commerce

The online second-hand vehicle sales models now include: auction model, consignment sales model, purchase and sales model, third party evaluation platform model and information consultant platform model. So choose a right sales model is important for sellers. We use AHP method to confirm key factors and built a score model base for different sales models. Though analysis, we can the conclusion that the best order of choice for online second-hand vehicle business model is: auction model, consignment sales model, purchase and sale model, information consultant platform and third party evaluation platform

Research on Continuous Use of B2B Platform in Chinese Intelligent Engineering Companies Based on the Theory of Resource Complementary

What factors affect the continuous use of B2B platforms by intelligent engineering companies is an important issue. Based on the theory of resource complementarity, a model of influencing factors for intelligent engineering companies to continue using the B2B platform is constructed, and four influencing factors including the complementary resources given by the platform, the complementary resources given by the company, interaction quality, and exploration and exploitation capability are analyzed. Using SmartPLS 3.0 to analyze 217 survey data, the results show that the complementary resources given by the platform and companies have positive impact on the interaction quality, and the complementary resources given by the platform have positive effect on the exploration and exploitation capabilities. Interaction quality has positive impact on the company’s continuous use intention of the B2B platform. The complementary resources given by the company have no positive impact on the exploration and exploitation capabilities, and the exploration and exploitation capabilities have no positive impact on the company’s continuous use intention of the B2B platform. Finally, some suggestions are proposed to increase the company’s continuous use intention of B2B platform

MIMOCrypt: Multi-User Privacy-Preserving Wi-Fi Sensing via MIMO Encryption

Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing attempts or rely on sophisticated technologies to support only a single sensing user, rendering them impractical for multi-user scenarios. Moreover, these proposals all fail to exploit Wi-Fi's multiple-in multiple-out (MIMO) capability. To this end, we propose MIMOCrypt, a privacy-preserving Wi-Fi sensing framework to support realistic multi-user scenarios. To thwart unauthorized eavesdropping while retaining the sensing and communication capabilities for legitimate users, MIMOCrypt innovates in exploiting MIMO to physically encrypt Wi-Fi channels, treating the sensed human activities as physical plaintexts. The encryption scheme is further enhanced via an optimization framework, aiming to strike a balance among i) risk of eavesdropping, ii) sensing accuracy, and iii) communication quality, upon securely conveying decryption keys to legitimate users. We implement a prototype of MIMOCrypt on an SDR platform and perform extensive experiments to evaluate its effectiveness in common application scenarios, especially privacy-sensitive human gesture recognition.Comment: IEEE S&P 2024, 19 pages, 22 figures, including meta reviews and response

Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models

Data Pipeline plays an indispensable role in tasks such as modeling machine learning and developing data products. With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems. This paper focuses on exploring how to optimize data flow through automated machine learning methods by integrating AutoML with Data Pipeline. We will discuss how to leverage AutoML technology to enhance the intelligence of Data Pipeline, thereby achieving better results in machine learning tasks. By delving into the automation and optimization of Data flows, we uncover key strategies for constructing efficient data pipelines that can adapt to the ever-changing data landscape. This not only accelerates the modeling process but also provides innovative solutions to complex problems, enabling more significant outcomes in increasingly intricate data domains. Keywords- Data Pipeline Training;AutoML; Data environment; Machine learnin

HMIAN: a Hierarchical Mapping and Interactive Attention Data Fusion Network for Traffic Forecasting

© 2022 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/JIOT.2022.3196461With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors such as weather and holidays on traffic is an unsolved problem. This paper proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing functional zones, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into functional zones, calculate the interaction between functional zones and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show that the proposed model better prediction performance compared with other existing approaches in more complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.Peer reviewe

Structural pathway for nucleation and growth of topologically close-packed phase from parent hexagonal crystal

The solid diffusive phase transformation involving the nucleation and growth of one nucleus is universal and frequently employed but has not yet been fully understood at the atomic level. Here, our first-principles calculations reveal a structural formation pathway of a series of topologically close-packed (TCP) phases within the hexagonally close-packed (hcp) matrix. The results show that the nucleation follows a nonclassical nucleation process, and the whole structural transformation is completely accomplished by the shuffle-based displacements, with a specific 3-layer hcp-ordering as the basic structural transformation unit. The thickening of plate-like TCP phases relies on forming these hcp-orderings at their coherent TCP/matrix interface to nucleate ledge, but the ledge lacks the dislocation characteristics considered in the conventional view. Furthermore, the atomic structure of the critical nucleus for the Mg2Ca and MgZn2 Laves phases was predicted in terms of Classical Nucleation Theory (CNT), and the formation of polytypes and off-stoichiometry in TCP precipitates is found to be related to the nonclassical nucleation behavior. Based on the insights gained, we also employed high-throughput screening to explore several common hcp-metallic (including hcp-Mg, Ti, Zr, and Zn) systems that may undergo hcp-to-TCP phase transformations. These insights can deepen our understanding of solid diffusive transformations at the atomic level, and constitute a foundation for exploring other technologically important solid diffusive transformations

Back Analysis of Rock Hydraulic Fracturing by Coupling Numerical Model and Computational Intelligent Technology

Hydraulic fracturing is widely used to determine in situ stress of rock engineering. In this paper we propose a new method for simultaneously determining the in situ stress and elastic parameters of rock. The method utilizing the hydraulic fracturing numerical model and a computational intelligent method is proposed and verified. The hydraulic fracturing numerical model provides the samples which include borehole pressure, in situ stress, and elastic parameters. A computational intelligent method is applied in back analysis. A multioutput support vector machine is used to map the complex, nonlinear relationship between the in situ stress, elastic parameters, and borehole pressure. The artificial bee colony algorithm is applied in back analysis to find the optimal in situ stress and elastic parameters. The in situ stress is determined using the proposed method and the results are compared with those of the classic breakdown formula. The proposed method provides a good estimate of the relationship between the in situ stress and borehole pressure and predicts the maximum horizontal in situ stress with high precision while considering the influence of pore pressure without the need to estimate Biot’s coefficient and other parameters

An Angular Position-Based Two-Stage Friction Modeling and Compensation Method for RV Transmission System

In RV transmission system (RVTS), friction is closely related to rotational speed and angular position. However, classical friction models do not consider the influence of angular position on friction, resulting in limited accuracy in describing the RVTS frictional behavior. For this reason, this paper proposes an angular position-based two-stage friction model for RVTS, and achieves a more accurate representation of friction of RVTS. The proposed model consists of two parts, namely pre-sliding model and sliding model, which are divided by the maximum elastic deformation recovery angle of RVTS obtained from loading-unloading tests. The pre-sliding friction behavior is regarded as a spring model, whose stiffness is determined by the angular position and the acceleration when the velocity crosses zero, while the sliding friction model is established by the angular-segmented Stribeck function, and the friction parameters of the adjacent segment are linearly smoothed. A feedforward compensation based on the proposed model was performed on the RVTS, and its control performance was compared with that using the classical Stribeck model. The comparison results show that when using the proposed friction model, the low-speed-motion smoothness of the RVTS can be improved by 14.2%, and the maximum zero-crossing speed error can be reduced by 37.5%, which verifies the validity of the proposed friction model, as well as the compensation method