“Share for bargaining?”: A willingness model based on privacy computing theory

The use of mobile coupons to share for bargaining has become an important marketing method for merchants in the field of e-commerce. However, there are still some shortcomings in the existing research on consumers’ willingness to share mobile coupons. First of all, the use and sharing of mobile coupons are analyzed separately. Secondly, most of theories and models in this domain derive from the field of knowledge. Lastly, the influence of different platforms on consumers’ willingness to share are not considered. Therefore, this paper explores the influencing factors of consumers’ willingness to share mobile coupons in different platform scenarios from the perspective of privacy computing, and proposes six hypotheses to construct a structural equation model. Further analysis of 270 valid questionnaires obtained under five scenarios shows that users’ perceived economic benefits and perceived social benefits have a significant positive impact on users’ willingness to share for bargaining, users’ perceived privacy risks have no significant impact on users’ willingness to share for bargaining, and users’ perceived social risks have a significant negative impact on users’ willingness to share for bargaining. Low share for bargaining links will weaken the negative impact of perceived social risk on sharing willingness

Research on the Design of Museum Cultural and Creative Products under the background of new cultural and Creative culture

This paper discusses the design of museum cultural and creative products under the background of new cultural and creative culture. Through the interpretation of related concepts of new cultural and creative products, from the aspects of its content and products combined with the current development of museum cultural and creative products, summed up the design principles of museum cultural and creative products. While interpreting the relevant design principles, this paper designs cultural and creative products by combining the cultural relics collected by several museums in Hubei Province, and provides feasible and constructive ideas and methods for the design of cultural and creative products in museums by combining theory with practice

Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding

This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose "Skeleton-of-Thought" (SoT), which guides LLMs to first generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-up (up to 2.39x across 11 different LLMs), but it can also potentially improve the answer quality on several question categories in terms of diversity and relevance. SoT is an initial attempt at data-centric optimization for efficiency, and reveal the potential of pushing LLMs to think more like a human for answer quality.Comment: Technical report, work in progres

Understanding Edge-of-Stability Training Dynamics with a Minimalist Example

Recently, researchers observed that gradient descent for deep neural networks operates in an ``edge-of-stability'' (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) is often larger than stability threshold 2/$\eta$ (where $\eta$ is the step size). Despite this, the loss oscillates and converges in the long run, and the sharpness at the end is just slightly below $2/\eta$. While many other well-understood nonconvex objectives such as matrix factorization or two-layer networks can also converge despite large sharpness, there is often a larger gap between sharpness of the endpoint and $2/\eta$. In this paper, we study EoS phenomenon by constructing a simple function that has the same behavior. We give rigorous analysis for its training dynamics in a large local region and explain why the final converging point has sharpness close to $2/\eta$. Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets.Comment: 53 pages, 19 figure

Optimal Repair Strategy Against Advanced Persistent Threats Under Time-Varying Networks

Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term cyberattack that has brought severe financial losses and critical infrastructure damages. Existing works mainly focus on APT defense under stable network topologies, while the problem under time-varying dynamic networks (e.g., vehicular networks) remains unexplored, which motivates our work. Besides, the spatiotemporal dynamics in defense resources, complex attackers' lateral movement behaviors, and lack of timely defense make APT defense a challenging issue under time-varying networks. In this paper, we propose a novel game-theoretical APT defense approach to promote real-time and optimal defense strategy-making under both periodic time-varying and general time-varying environments. Specifically, we first model the interactions between attackers and defenders in an APT process as a dynamic APT repair game, and then formulate the APT damage minimization problem as the precise prevention and control (PPAC) problem. To derive the optimal defense strategy under both latency and defense resource constraints, we further devise an online optimal control-based mechanism integrated with two backtracking-forward algorithms to fastly derive the near-optimal solution of the PPAC problem in real time. Extensive experiments are carried out, and the results demonstrate that our proposed scheme can efficiently obtain optimal defense strategy in 54481 ms under seven attack-defense interactions with 9.64$\%$ resource occupancy in stimulated periodic time-varying and general time-varying networks. Besides, even under static networks, our proposed scheme still outperforms existing representative APT defense approaches in terms of service stability and defense resource utilization