Alcohol consumption has a significant impact on individuals' health, with
even more pronounced consequences when consumption becomes excessive. One
approach to promoting healthier drinking habits is implementing just-in-time
interventions, where timely notifications indicating intoxication are sent
during heavy drinking episodes. However, the complexity or invasiveness of an
intervention mechanism may deter an individual from using them in practice.
Previous research tackled this challenge using collected motion data and
conventional Machine Learning (ML) algorithms to classify heavy drinking
episodes, but with impractical accuracy and computational efficiency for mobile
devices. Consequently, we have elected to use Hyperdimensional Computing (HDC)
to design a just-in-time intervention approach that is practical for
smartphones, smart wearables, and IoT deployment. HDC is a framework that has
proven results in processing real-time sensor data efficiently. This approach
offers several advantages, including low latency, minimal power consumption,
and high parallelism. We explore various HDC encoding designs and combine them
with various HDC learning models to create an optimal and feasible approach for
mobile devices. Our findings indicate an accuracy rate of 89\%, which
represents a substantial 12\% improvement over the current state-of-the-art.

酒精消耗对个人健康有重大影响，过度饮酒时后果更为显著。实施及时干预的健康饮酒习惯是一种促进方法，通过在大量饮酒时发送提示醉酒的及时通知。本研究旨在使用超维计算技术设计一种适用于智能手机、智能可穿戴设备和物联网部署的即时干预方法，通过研究不同的超维编码设计和学习模型，实现低延迟、最小功耗和高并行性。研究结果显示，准确率达到 89％，比当前最先进的技术提高了 12％。

使用嵌入式设备上的超维度计算增强皮肤透醇水平检测

Enhanced Detection of Transdermal Alcohol Levels Using Hyperdimensional  Computing on Embedded Devices

Large Language Models (LLM) have taken the front seat in most of the news
since November 2023, when ChatGPT was introduced. After more than one year, one
of the major reasons companies are resistant to adopting them is the limited
confidence they have in the trustworthiness of those systems. In a study by
(Baymard, 2023), ChatGPT-4 showed an 80.1% false-positive error rate in
identifying usability issues on websites. A Jan. '24 study by JAMA Pediatrics
found that ChatGPT has an accuracy rate of 17% percent when diagnosing
pediatric medical cases (Barile et al., 2024). But then, what is "trust"? Trust
is a relative, subject condition that can change based on culture, domain,
individuals. And then, given a domain, how can the trustworthiness of a system
be measured? In this paper, I present a systematic approach to measure
trustworthiness based on a predefined ground truth, represented as a knowledge
graph of the domain. The approach is a process with humans in the loop to
validate the representation of the domain and to fine-tune the system.
Measuring the trustworthiness would be essential for all the entities
operating in critical environments, such as healthcare, defense, finance, but
it would be very relevant for all the users of LLMs.

基于预定义的领域知识图谱，本文提出了一种系统的方法来衡量大型语言模型的可信度，其过程中人类参与验证和优化系统，该方法对于在卫生保健、国防、金融等关键环境中运营的实体以及所有大型语言模型用户十分重要。

真相告诉我：一个用于测量大型语言模型可信度的系统

Tell me the truth: A system to measure the trustworthiness of Large  Language Models

In this study, we investigated the ability of the large language model (LLM)
to enhance healthcare data interoperability. We leveraged the LLM to convert
clinical texts into their corresponding FHIR resources. Our experiments,
conducted on 3,671 snippets of clinical text, demonstrated that the LLM not
only streamlines the multi-step natural language processing and human
calibration processes but also achieves an exceptional accuracy rate of over
90% in exact matches when compared to human annotations.

通过研究使用大型语言模型（LLM）来增强医疗数据互操作性的能力，我们发现 LLM 可以将临床文本转化为相应的 FHIR 资源。通过对 3,671 段临床文本进行实验，我们证明 LLM 不仅简化了多步骤的自然语言处理和人工校准过程，而且在与人类标注相比较时，在准确匹配方面达到了超过 90% 的异常准确率。