We present ABSApp, a portable system for weakly-supervised aspect-based
sentiment extraction. The system is interpretable and user friendly and does
not require labeled training data, hence can be rapidly and cost-effectively
used across different domains in applied setups. The system flow includes three
stages: First, it generates domain-specific aspect and opinion lexicons based
on an unlabeled dataset; second, it enables the user to view and edit those
lexicons (weak supervision); and finally, it enables the user to select an
unlabeled target dataset from the same domain, classify it, and generate an
aspect-based sentiment report. ABSApp has been successfully used in a number of
real-life use cases, among them movie review analysis and convention impact
analysis.

ABSApp 是一个便于使用且可解释的便携式系统，用于弱监督的基于方面的情感提取，可快速且经济有效地用于不同领域的应用设置中，其流程包括三个阶段：基于未标记数据集生成特定于领域的方面和观点词典；使用户能够查看和编辑这些词典（弱监督）；使用户能够选择来自同一领域的未标记目标数据集，对其进行分类，并生成基于方面的情感报告。ABSApp 已经成功应用于大量真实生活案例，其中包括电影评论分析和会议影响分析。

ABSApp：一款可移植的弱监督基于方面的情感抽取系统

ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction  System

Hand gesture recognition has long been a hot topic in human computer
interaction. Traditional camera-based hand gesture recognition systems cannot
work properly under dark circumstances. In this paper, a Doppler Radar based
hand gesture recognition system using convolutional neural networks is
proposed. A cost-effective Doppler radar sensor with dual receiving channels at
5.8GHz is used to acquire a big database of four standard gestures. The
received hand gesture signals are then processed with time-frequency analysis.
Convolutional neural networks are used to classify different gestures.
Experimental results verify the effectiveness of the system with an accuracy of
98%. Besides, related factors such as recognition distance and gesture scale
are investigated.

本文提出了一种使用双通道 Doppler 雷达和卷积神经网络的成本效益高的手势识别系统，其在时间 - 频率分析后对手势信号进行分类。实验结果表明，该系统具有 98％的准确性。

基于卷积神经网络的多普勒雷达手势识别系统

Doppler-Radar Based Hand Gesture Recognition System Using Convolutional  Neural Networks

As machine learning algorithms enter applications in industrial settings,
there is increased interest in controlling their cpu-time during testing. The
cpu-time consists of the running time of the algorithm and the extraction time
of the features. The latter can vary drastically when the feature set is
diverse. In this paper, we propose an algorithm, the Greedy Miser, that
incorporates the feature extraction cost during training to explicitly minimize
the cpu-time during testing. The algorithm is a straightforward extension of
stage-wise regression and is equally suitable for regression or multi-class
classification. Compared to prior work, it is significantly more cost-effective
and scales to larger data sets.

提出了用于机器学习应用中的算法 —— 贪心守财奴算法，能够在训练期间控制特征提取成本，从而在测试期间明确地最小化 CPU 时间以实现成本效益。相比之前的工作，它具有显着的成本效益，并适用于更大的数据集。