Synthetic data generation is increasingly recognized as a crucial solution to
address data related challenges such as scarcity, bias, and privacy concerns.
As synthetic data proliferates, the need for a robust evaluation framework to
select a synthetic data generator becomes more pressing given the variety of
options available. In this research study, we investigate two primary
questions: 1) How can we select the most suitable synthetic data generator from
a set of options for a specific purpose? 2) How can we make the selection
process more transparent, accountable, and auditable? To address these
questions, we introduce a novel approach in which the proposed ranking
algorithm is implemented as a smart contract within a permissioned blockchain
framework called Sawtooth. Through comprehensive experiments and comparisons
with state-of-the-art baseline ranking solutions, our framework demonstrates
its effectiveness in providing nuanced rankings that consider both desirable
and undesirable properties. Furthermore, our framework serves as a valuable
tool for selecting the optimal synthetic data generators for specific needs
while ensuring compliance with data protection principles.

通过实施智能合约的新型排名算法，我们的研究框架为选择特定需求下最佳合成数据生成器提供了一种透明、可追溯和可核算的方法，并在考虑可取和不可取属性的情况下展示了其效果，同时确保遵守数据保护原则。

基于许可区块链的合成数据生成器排名框架

Permissioned Blockchain-based Framework for Ranking Synthetic Data  Generators

As the popularity of adhesive joints in industry increases, so does the need
for tools to support the process of selecting a suitable adhesive. While some
such tools already exist, they are either too limited in scope, or offer too
little flexibility in use. This work presents a more advanced tool, that was
developed together with a team of adhesive experts. We first extract the
experts' knowledge about this domain and formalize it in a Knowledge Base (KB).
The IDP-Z3 reasoning system can then be used to derive the necessary
functionality from this KB. Together with a user-friendly interactive
interface, this creates an easy-to-use tool capable of assisting the adhesive
experts. To validate our approach, we performed user testing in the form of
qualitative interviews. The experts are very positive about the tool, stating
that, among others, it will help save time and find more suitable adhesives.
Under consideration in Theory and Practice of Logic Programming (TPLP).

通过与粘接专家团队的合作，本研究提出了一种高级工具，包括一个知识库和交互界面，能够协助粘接专家节省时间并找到更合适的胶粘剂。

基于知识的黏合剂选择支持：能否粘合？

Knowledge-Based Support for Adhesive Selection: Will it Stick?

We study the problem of selecting a member of a set of agents based on
impartial nominations by agents from that set. The problem was studied
previously by Alon et al. and Holzman and Moulin and has important applications
in situations where representatives are selected from within a group or where
publishing or funding decisions are made based on a process of peer review. Our
main result concerns a randomized mechanism that in expectation awards the
prize to an agent with at least half the maximum number of nominations. Subject
to impartiality, this is best possible.

研究代理人选择问题，提出一种随机机制以公正且高效的选择代表，可应用于同行评议、人员选拔等情况。