Investors are interested in predicting future success of startup companies,
preferably using publicly available data which can be gathered using free
online sources. Using public-only data has been shown to work, but there is
still much room for improvement. Two of the best performing prediction
experiments use 17 and 49 features respectively, mostly numeric and categorical
in nature. In this paper, we significantly expand and diversify both the
sources and the number of features (to 171) to achieve better prediction. Data
collected from Crunchbase, the Google Search API, and Twitter (now X) are used
to predict whether a company will raise a round of funding within a fixed time
horizon. Much of the new features are textual and the Twitter subset include
linguistic metrics such as measures of passive voice and parts-of-speech. A
total of ten machine learning models are also evaluated for best performance.
The adaptable model can be used to predict funding 1-5 years into the future,
with a variable cutoff threshold to favor either precision or recall.
Prediction with comparable assumptions generally achieves F scores above 0.730
which outperforms previous attempts in the literature (0.531), and does so with
fewer examples. Furthermore, we find that the vast majority of the performance
impact comes from the top 18 of 171 features which are mostly generic company
observations, including the best performing individual feature which is the
free-form text description of the company.

通过扩大和丰富数据源以及特征数量，利用机器学习模型对创业公司的公开数据进行预测，可较准确地预测公司是否能在未来一定时间内筹集到资金。

通过文本分析提升创业成功率

Improving Startup Success with Text Analysis

The evolution of wireless communications into 6G and beyond is expected to
rely on new machine learning (ML)-based capabilities. These can enable
proactive decisions and actions from wireless-network components to sustain
quality-of-service (QoS) and user experience. Moreover, new use cases in the
area of vehicular and industrial communications will emerge. Specifically in
the area of vehicle communication, vehicle-to-everything (V2X) schemes will
benefit strongly from such advances. With this in mind, we have conducted a
detailed measurement campaign with the purpose of enabling a plethora of
diverse ML-based studies. The resulting datasets offer GPS-located wireless
measurements across diverse urban environments for both cellular (with two
different operators) and sidelink radio access technologies, thus enabling a
variety of different studies towards V2X. The datasets are labeled and sampled
with a high time resolution. Furthermore, we make the data publicly available
with all the necessary information to support the on-boarding of new
researchers. We provide an initial analysis of the data showing some of the
challenges that ML needs to overcome and the features that ML can leverage, as
well as some hints at potential research studies.

通过一系列的详细的测量活动，我们提供了一批适用于车联网 (V2X) 领域的、具有高时间分辨率的 GPS 定位的无线测量数据集，并公开了这些数据，供新研究人员使用。这些数据可用于针对 6G 及以后的无线通信技术的机器学习研究和新的车联网和工业通信用例的探索，并建议 ML 需要克服的一些挑战和利用的一些功能。}

Berlin V2X: 多车和多无线电接入技术的机器学习数据集

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

Automatic speech recognition (ASR) systems play a key role in many commercial
products including voice assistants. Typically, they require large amounts of
clean speech data for training which gives an undue advantage to large
organizations which have tons of private data. In this paper, we have first
curated a fairly big dataset using publicly available data sources. Thereafter,
we tried to investigate if we can use publicly available noisy data to train
robust ASR systems. We have used speech enhancement to clean the noisy data
first and then used it together with its cleaned version to train ASR systems.
We have found that using speech enhancement gives 9.5\% better word error rate
than training on just noisy data and 9\% better than training on just clean
data. It's performance is also comparable to the ideal case scenario when
trained on noisy and its clean version.

本研究旨在研究使用公开的噪声数据训练自动语音识别（ASR）系统的可行性，并发现使用语音增强技术进行数据清理后训练 ASR 系统比仅使用噪声数据和纯净数据训练 ASR 系统分别提高了 9.5％和 9％的单词错误率，其表现与噪声数据和纯净数据训练的理想情况相当。