Virtual commerce applies immersive technology such as augmented reality and virtual reality into e-commerce to shift consumer perception from 2D product catalogs to 3D immersive virtual spaces. In virtual commerce, the alignment of application design paradigms and the factors influencing consumer behavior is paramount to promote purchase of products and services. The question of their relation needs to be answered, together with the possible improvement of application design. This paper used a systematic literature review approach to synthesize research on virtual commerce from both application design and consumer behavior research, considering the promotion of purchase in virtual commerce settings. Throughout the review, influential factors to purchase and preeminent design artifacts were identified. Then, the research gaps were discovered by mapping the design artifacts to the influential factors, which can inspire future research opportunities on the synergy of these two research directions. Moreover, the evolution of virtual commerce research along with multiple directions were discussed, including the suggestion of meta-commerce as a future trend.

In the last decade, blockchain smart contracts emerged as an automated, decentralized, traceable, and immutable medium of value exchange. Nevertheless, existing blockchain smart contracts are not compatible with legal contracts. The automatic execution of a legal contract written in natural language is an open research question that can extend the blockchain ecosystem and inspire next-era business paradigms. In this paper, we propose an AI-assisted Smart Contract Generation (AIASCG) framework that allows contracting parties in heterogeneous contexts and different languages to collaboratively negotiate and draft the contract clauses. AIASCG provides a universal representation of contracts through the machine natural language (MNL) as the common understanding of the contract obligations. We compare the design of AIASCG with existing smart contract generation approaches to present its novelty. The main contribution of AIASCG is to address the issue in our previous proposed smart contract generation framework. For sentences written in natural language, existing framework requires editors to manually split sentences into words with semantic meaning. We propose an AI-based automatic word segmentation technique called Separation Inference (SpIn) to fulfill automatic split of the sentence. SpIn serves as the core component in AIASCG that accurately recommends the intermediate MNL outputs from a natural language sentence, tremendously reducing the manual effort in contract generation. SpIn is evaluated from a robustness and human satisfaction point of view to demonstrate its effectiveness. In the robustness evaluation, SpIn achieves state-of-the-art F1 scores and Recall of Out-of-Vocabulary (R_OOV) words on multiple word segmentation tasks. In addition, in the human evaluation, participants believe that 88.67% of sentences can be saved 80–100% of the time through automatic word segmentation.

In recent years, various indoor social security emergencies have brought major changes to people’s lives. However, the state-of-art surveillance technology achieving object tracking alone is insufficient through a single visual tracking method and may cause inaccurate tracking (such as burred faces, loss target, and occlusion) and is time-consuming for a long video sequence. Accurate tracking and localization systems are essential for developing indoor localization systems. This paper designs a fusing communication system called the Internet of Electronic-Visual Things Localization system (IoEVT), which simultaneously tracks targets assisting with an Adaptive Kalman Filter (AKF). The data fusion indoor localization approach using AKF fuses multi-data from electronic (E) and visual (V) data, such as WiFi and cameras. The fusing method significantly improves EV data sensing abilities for solving occluded visual information when only using single V data in occlusion scenarios and reduces the localization error rate. In addition, our real-world experimental validation shows that the proposed IoEVT system achieves better accuracy, scalability, and robustness with low network bandwidth consumption compared with the single E or V data in many scenarios, such as occlusion and occlusion-free.