In South Africa, there is an ever-growing issue of vehicle hijackings. This
leads to travellers constantly being in fear of becoming a victim to such an
incident. This work presents a new semi-supervised approach to using tweets to
identify hijacking incidents by using unsupervised anomaly detection
algorithms. Tweets consisting of the keyword "hijacking" are obtained, stored,
and processed using the term frequency-inverse document frequency (TF-IDF) and
further analyzed by using two anomaly detection algorithms: 1) K-Nearest
Neighbour (KNN); 2) Cluster Based Outlier Factor (CBLOF). The comparative
evaluation showed that the KNN method produced an accuracy of 89%, whereas the
CBLOF produced an accuracy of 90%. The CBLOF method was also able to obtain a
F1-Score of 0.8, whereas the KNN produced a 0.78. Therefore, there is a slight
difference between the two approaches, in favour of CBLOF, which has been
selected as a preferred unsupervised method for the determination of relevant
hijacking tweets. In future, a comparison will be done between supervised
learning methods and the unsupervised methods presented in this work on larger
dataset. Optimisation mechanisms will also be employed in order to increase the
overall performance.

本研究提出了一种新的半监督方法，利用推特数据和无监督异常检测算法识别车辆劫持事件，比较评估结果表明 Cluster Based Outlier Factor 方法在准确性和 F1-Score 上优于 K-Nearest Neighbour，因此 CBLOF 方法被选择作为更好的无监督方法，并计划在更大的数据集上使用监督学习方法进行比较和优化。

用于确定车辆劫持推文的半监督异常检测

Semi-Supervised Anomaly Detection for the Determination of Vehicle  Hijacking Tweets

Internet traffic volume estimation has a significant impact on the business
policies of the ISP (Internet Service Provider) industry and business
successions. Forecasting the internet traffic demand helps to shed light on the
future traffic trend, which is often helpful for ISPs decision-making in
network planning activities and investments. Besides, the capability to
understand future trend contributes to managing regular and long-term
operations. This study aims to predict the network traffic volume demand using
deep sequence methods that incorporate Empirical Mode Decomposition (EMD) based
noise reduction, Empirical rule based outlier detection, and $K$-Nearest
Neighbour (KNN) based outlier mitigation. In contrast to the former studies,
the proposed model does not rely on a particular EMD decomposed component
called Intrinsic Mode Function (IMF) for signal denoising. In our proposed
traffic prediction model, we used an average of all IMFs components for signal
denoising. Moreover, the abnormal data points are replaced by $K$ nearest data
points average, and the value for $K$ has been optimized based on the KNN
regressor prediction error measured in Root Mean Squared Error (RMSE). Finally,
we selected the best time-lagged feature subset for our prediction model based
on AutoRegressive Integrated Moving Average (ARIMA) and Akaike Information
Criterion (AIC) value. Our experiments are conducted on real-world internet
traffic datasets from industry, and the proposed method is compared with
various traditional deep sequence baseline models. Our results show that the
proposed EMD-KNN integrated prediction models outperform comparative models.

本研究旨在使用深度序列方法来预测网络流量需求，其中包括基于经验模态分解（EMD）的噪声抑制、基于经验规则的异常值检测和基于 K - 最近邻的异常值缓解。

DEK-Forecaster: 融合 EMD-KNN 的新型深度学习模型用于交通预测

DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for  Traffic Prediction

It is well understood that Dynamic Time Warping (DTW) is effective in
revealing similarities between time series that do not align perfectly. In this
paper, we illustrate this on spectroscopy time-series data. We show that DTW is
effective in improving accuracy on a regression task when only a single
wavelength is considered. When combined with k-Nearest Neighbour, DTW has the
added advantage that it can reveal similarities and differences between samples
at the level of the time-series. However, in the problem, we consider here data
is available across a spectrum of wavelengths. If aggregate statistics (means,
variances) are used across many wavelengths the benefits of DTW are no longer
apparent. We present this as another example of a situation where big data
trumps sophisticated models in Machine Learning.

本文研究了使用 Dynamic Time Warping 方法对光谱时间序列数据进行相似性比对，并发现结合 k-Nearest Neighbour 算法可以提高准确性，但当数据跨越多个波长时，如果使用了平均值和方差等数据聚合统计方法，DTW 的效果将不明显，因此在机器学习中，有时候大数据胜过复杂模型。