Automating the generation of multiple linked view visualization is imperative for improving data analysis efficiency. Large Language Models (LLMs) offer substantial potential for enabling this automation, yet they encounter notable challenges in understanding complex queries and producing relevant interactive visualizations. To tackle these challenges, we introduce SmartMLVs, a system designed to harness LLMs for automatic interactive multiple linked views generation with human guidance. First, we analyze the challenges LLMs may encounter when designing visualizations in place of experts. To address these challenges, we gather the essential domain knowledge required for visual analysis process and propose a framework consisting of decomposition, visualization and linking. The decomposition process applies a human-AI interaction method to clarify user requirements. For each decomposed question, the generation process handles chart type selection, data processing and visualization generation. Finally, the linking process adds interactions for views and provides users with data insights. For better human-AI collaboration, we design a system for data exploration. Our system applies the entire framework, supporting users’ interactive exploration with multiple linked views, and can iteratively generate linked views based on user feedback. We examine the effectiveness of our method through usage scenarios and evaluations.

In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. The implementation of Internet of Things (IoT) makes it possible to take real-time production state as an important factor affecting PP. In the IoT-enabled workshop, a two-stage transfer learning-based prediction method using both historical production data and real-time state data is proposed to solve the problem of low-prediction accuracy and poor generalization performance caused by insufficient data of target order. The deep autoencoder (DAE) model with transfer learning is designed to extract the generalized features of target order in the first stage, which uses bootstrap sampling to avoid over fitting. The deep belief network (DBN) model with transfer learning is constructed to fit the nonlinear relation for PP prediction in the second stage. A real case from an IoT enabled machining workshop is taken to validate the performance of the proposed method over the other methods such as DBN, deep neural network.

As blockchain technologies mature and ecosystems over blockchain evolve, peers on blockchain networks often face situations in which they need to conduct voting for decision-making; as happened in the case of the DAO hard fork event on Ethereum. However, a natively built-in voting mechanism is not available on any of the existing blockchain platforms. Thus, the decision making either is delegated to a few network members who make such decisions offline or is dependent on third party online voting services. In both cases, peers directly or indirectly rely on trusted parties or centralized systems. This is against the basic decentralization principle of blockchain and exposes the election to frauds. To facilitate decision-making in a decentralized and secure manner, we propose a native blockchain voting protocol for peers to vote over their existing blockchain network without the need of any trusted or third party. Our protocol preserves end-to-end privacy and possesses desirable properties such as detectability and correctability against cheating. A reference implementation of our protocol on Hyperledger Fabric that demonstrates the validity and practical applicability of our protocol is also provided.