Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media. Our approach embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.

Due to the severity of the social media offensive and hateful comments in Brazil, and the lack of research in Portuguese, this paper provides the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection. The HateBR corpus was collected from the comment section of Brazilian politicians' accounts on Instagram and manually annotated by specialists, reaching a high inter-annotator agreement. The corpus consists of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level classification (highly, moderately, and slightly offensive), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). We also implemented baseline experiments for offensive language and hate speech detection and compared them with a literature baseline. Results show that the baseline experiments on our corpus outperform the current state-of-the-art for the Portuguese language.

当歌曲创作或演奏时，歌手/词曲作者通常会出现通过它表达感受或情感的意图。对于人类而言，将音乐作品或表演中的情感与观众的主观感知相匹配可能会非常具有挑战性。幸运的是，此问题的机器学习方法更简单。通常，它需要一个数据集，从该数据集中提取音频功能以将此信息呈现给数据驱动的模型，从而又将训练以预测给定歌曲与目标情绪匹配的概率是什么。在本文中，我们研究了最近出版物中最常见的功能和模型来解决此问题，揭示了哪些最适合在无伴奏歌曲中识别情感。

恶意软件是对计算机系统的主要威胁，并对网络安全构成了许多挑战。有针对性的威胁（例如勒索软件）每年造成数百万美元的损失。恶意软件感染的不断增加一直激励流行抗病毒（AV）制定专用的检测策略，其中包括精心制作的机器学习（ML）管道。但是，恶意软件开发人员不断地将样品的功能更改为绕过检测。恶意软件样品的这种恒定演变导致数据分布（即概念漂移）直接影响ML模型检测率，这是大多数文献工作中未考虑的。在这项工作中，我们评估了两个Android数据集的概念漂移对恶意软件分类器的影响：DREBIN（约130k应用程序）和Androzoo（约350K应用程序）的子集。我们使用这些数据集训练自适应随机森林（ARF）分类器以及随机梯度下降（SGD）分类器。我们还使用其Virustotal提交时间戳订购了所有数据集样品，然后使用两种算法（Word2Vec和tf-idf）从其文本属性中提取功能。然后，我们进行了实验，以比较两个特征提取器，分类器以及四个漂移检测器（DDM，EDDM，ADWIN和KSWIN），以确定真实环境的最佳方法。最后，我们比较一些减轻概念漂移的可能方法，并提出了一种新的数据流管道，该管道同时更新分类器和特征提取器。为此，我们通过（i）对9年来收集的恶意软件样本进行了纵向评估（2009- 2018年），（ii）审查概念漂移检测算法以证明其普遍性，（iii）比较不同的ML方法来减轻此问题，（iv）提出了超过文献方法的ML数据流管道。

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.