随着在线社交媒体提供的沟通自由，仇恨言论越来越多地产生。这导致网络冲突影响个人和国家一级的社会生活。结果，在发送到社交网络之前，仇恨的内容分类越来越需要过滤仇恨内容。本文着重于使用多个深层模型在社交媒体中对仇恨言论进行分类，这些模型通过整合了最近的基于变压器的语言模型，例如BERT和神经网络。为了改善分类性能，我们通过几种合奏技术进行了评估，包括软投票，最大价值，硬投票和堆叠。我们使用了三个公开可用的Twitter数据集（Davidson，Hateval2019，OLID）来识别进攻性语言。我们融合了所有这些数据集以生成单个数据集（DHO数据集），该数据集在不同的标签上更加平衡，以执行多标签分类。我们的实验已在Davidson数据集和Dho Corpora上举行。后来给出了最佳的总体结果，尤其是F1宏观分数，即使它需要更多的资源（时间执行和内存）。实验显示了良好的结果，尤其是整体模型，其中堆叠在Davidson数据集上的F1得分为97％，并且在DHO数据集上汇总合奏的77％。

Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics-based approaches are often used to characterize the degradation behavior analytically, yet explicit domain knowledge and accurate mathematical models are required. Building such models can be very challenging due to a lack of a full understanding of the complex physical processes inducing the degradation under various operating conditions. To overcome the aforementioned limitations, we propose a new data-driven approach, extracting useful insights from the operational monitored data to predict the degradation trend without requiring any specific knowledge or using any physical model. The proposed approach is based on an unsupervised technique, a conditional variational autoencoder, and validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge emitting laser reliability data. The experimental results confirm that our model (i) achieves a good degradation prediction and generalization performance by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based anomaly detection techniques, and (iii) helps to shorten the aging tests by early predicting the failed devices before the end of the test and thereby saving costs

Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great deal of attention has been devoted to improving it and thereby ensuring reliable transmission. In this paper, a predictive maintenance framework using machine learning techniques is proposed for real-time heath monitoring and prognosis of semiconductor laser and thus enhancing its reliability. The proposed approach is composed of three stages: i) real-time performance degradation prediction, ii) degradation detection, and iii) remaining useful life (RUL) prediction. First of all, an attention based gated recurrent unit (GRU) model is adopted for real-time prediction of performance degradation. Then, a convolutional autoencoder is used to detect the degradation or abnormal behavior of a laser, given the predicted degradation performance values. Once an abnormal state is detected, a RUL prediction model based on attention-based deep learning is utilized. Afterwards, the estimated RUL is input for decision making and maintenance planning. The proposed framework is validated using experimental data derived from accelerated aging tests conducted for semiconductor tunable lasers. The proposed approach achieves a very good degradation performance prediction capability with a small root mean square error (RMSE) of 0.01, a good anomaly detection accuracy of 94.24% and a better RUL estimation capability compared to the existing ML-based laser RUL prediction models.

终身机器学习或持续学习模型试图通过在一系列任务中累积知识来逐步学习。因此，这些模型学会更好，更快。它们用于各种智能系统，这些系统必须与人类或任何动态环境互动，例如，聊天和自驾车。更少的内存方法更常用于深度神经网络，该网络可容纳从其体系结构内的任务中的传入信息。它允许他们在所有已见的任务中表现良好。这些模型患有语义漂移或可塑性稳定性困境。现有模型使用Minkowski距离措施来确定要冻结，更新或重复的哪些节点。这些距离度量不提供更好的节点分离，因为它们易受高维稀疏向量。在我们提出的方法中，我们使用角距离来评估提供更好地分离节点的个体节点中的语义漂移，从而在稳定性和可塑性之间更好地平衡。所提出的方法通过在标准数据集上保持更高的准确性来实现最先进的模型。