Due to the advancements in artificial intelligence technology, chatbots have become essential tools in digital communication practices. Within the realm of e-commerce, many brands have already deployed chatbots as virtual agents to deliver consumer services and replace human service agents. Drawing on the theoretical framework of psychological ownership, the present study seeks to shed light on how consumers interact with chatbots by examining the effects of choosing chatbot avatars in an online shopping process. In our experiment, we discovered that participants who made active choices regarding their chatbot avatars experienced an increased feeling of psychological ownership. This enhanced ownership significantly boosted their trust and sense of autonomy, which in turn affected various e-commerce results. These findings emphasized consumers’ active control in affecting e-commerce effectiveness. We further discuss the theoretical and practical implications for chatbot marketing, as well as the broader impact of AI-driven e-commerce and digital communication.

One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not, we anticipate that an algorithm should demonstrate its ability to learn an effective control policy that seamlessly aligns with the inherent distribution of offline data. Unfortunately, behavior regularization, a simple yet effective offline RL algorithm, tends to struggle in this regard. In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. Our key observation is that by iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement, while also implicitly avoiding querying out-of-sample actions to prevent catastrophic learning failures. We prove that in the tabular setting this algorithm is capable of learning the optimal policy covered by the offline dataset, commonly referred to as the in-sample optimal policy. We then explore several implementation details of the algorithm when function approximations are applied. The resulting algorithm is easy to implement, requiring only a few lines of code modification to existing methods. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks, clearly demonstrate its superiority over behavior regularization.

BACKGROUND: Today's biomedical imaging technology has been able to present the morphological structure or functional metabolic information of organisms at different scale levels, such as organ, tissue, cell, molecule and gene. However, different imaging modes have different application scope, advantages and disadvantages. In order to improve the role of medical image in disease diagnosis, the fusion of biomedical image information at different imaging modes and scales has become an important research direction in medical image. Traditional medical image fusion methods are all designed to measure the activity level and fusion rules. They are lack of mining the context features of different modes of image, which leads to the obstruction of improving the quality of fused images. METHOD: In this paper, an attention-multiscale network medical image fusion model based on contextual features is proposed. The model selects five backbone modules in the VGG-16 network to build encoders to obtain the contextual features of medical images. It builds the attention mechanism branch to complete the fusion of global contextual features and designs the residual multiscale detail processing branch to complete the fusion of local contextual features. Finally, it completes the cascade reconstruction of features by the decoder to obtain the fused image. RESULTS: Ten sets of images related to five diseases are selected from the AANLIB database to validate the VANet model. Structural images are derived from MR images with high resolution and functional images are derived from SPECT and PET images that are good at describing organ blood flow levels and tissue metabolism. Fusion experiments are performed on twelve fusion algorithms including the VANet model. The model selects eight metrics from different aspects to build a fusion quality evaluation system to complete the performance evaluation of the fused images. Friedman's test and the post-hoc Nemenyi test are introduced to conduct professional statistical tests to demonstrate the superiority of VANet model. CONCLUSIONS: The VANet model completely captures and fuses the texture details and color information of the source images. From the fusion results, the metabolism and structural information of the model are well expressed and there is no interference of color information on the structure and texture; in terms of the objective evaluation system, the metric value of the VANet model is generally higher than that of other methods.; in terms of efficiency, the time consumption of the model is acceptable; in terms of scalability, the model is not affected by the input order of source images and can be extended to tri-modal fusion.

Helper thread prefetching have the potential of improving the performance of irregular data intensive applications, but the prefetching effect depends on how efficiently and swiftly the control parameters can be selected. The parameter selection and optimization was done by executing the application exhaustively in prior works. In this study, we propose a helper thread prefetching control framework, which adjusts the control parameters of helper thread automatically, called HPCF. We present the idea, initial design and implementation of HPCF. In particular, we establish a dynamic control model of helper thread prefetching and develop a two-level parameter selection algorithm. We evaluate the proposed HPCF framework on commodity multi-core platforms by using selected benchmarks which come from SPEC2006, Olden and SSCA2. Results show that our approach performs almost equal to our prior static Skip Helper Thread prefetching scheme, while the parameter selection was done by executing the application only once. And it achieves up to 33.3%, 18.2% and 18.6% performance improvement for MST, MCF, and SSCA2 benchmarks, respectively.