Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor. In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable. Experiments on 4 darkweb social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture. Our code is publicly available at : https://github.com/gayanku/NBC-Softmax

对比学习最近在包括图形在内的许多领域取得了巨大的成功。然而，对比损失，尤其是对于图形，需要大量的负样本，这些样本是不可计算的，并且在二次时复杂性具有计算性过高。子采样不是最佳和不正确的负抽样导致采样偏差。在这项工作中，我们提出了一种基于元节点的近似技术，该技术可以（a）代理二次群集大小的时间复杂性中的所有负组合（b），（c）在图级别，而不是节点级别，（d）利用图形稀疏性。通过用添加群集对替换节点对，我们在图表级别计算群集时间的负fertiations。最终的代理近似元节点对比度（PAMC）损失基于简单优化的GPU操作，可捕获完整的负面因素，但具有线性时间复杂性，但具有有效的效率。通过避免采样，我们有效地消除了样本偏差。我们符合大量样品的标准，从而实现了块对比度，这被证明超过了成对的损失。我们使用学习的软群集分配进行元节点收缩，并避免在边缘创建过程中添加可能的异质和噪声。从理论上讲，我们表明现实世界图表很容易满足我们近似所需的条件。从经验上讲，我们在6个基准测试上表现出对最先进的图形聚类的有希望的准确性。重要的是，我们在效率方面获得了可观的收益。训练时间最多可达3倍，推理时间为1.8倍，减少GPU记忆的时间超过5倍。

随着信用卡交易量的增长，欺诈百分比也在上升，包括机构打击和补偿受害者的间接费用。将机器学习用于金融部门可以更有效地保护欺诈和其他经济犯罪。经过适当训练的机器学习分类器有助于主动欺诈检测，改善利益相关者的信任和对非法交易的鲁棒性。但是，由于欺诈数据的极为不平衡的性质以及准确，完全完全确定欺诈行为的挑战，以创建金标准的地面真相，因此基于机器学习的欺诈检测算法的设计是具有挑战性和缓慢的。此外，没有基准或标准分类器评估指标来衡量和识别更好的性能分类器，从而使研究人员处于黑暗状态。在这项工作中，我们建立了一个理论基础，以模拟人类注释错误和现实世界欺诈检测数据集中典型的极端失衡。通过对假设分类器进行经验实验，并具有近似于流行的现实世界信用卡欺诈数据集的合成数据分布，我们模拟了人类注释错误和极端失衡，以观察流行的机器学习分类器评估矩阵的行为。我们证明，按照特定顺序，合并的F1分数和G均值是典型不平衡欺诈检测模型分类的最佳评估指标。

机器学习已为财务欺诈检测打开了新的工具。使用带注释的交易样本，机器学习分类算法学会了检测欺诈。随着信用卡交易量的不断增长和欺诈百分比的增加，人们越来越有兴趣寻找适当的机器学习分类器进行检测。但是，欺诈数据集是多种多样的，并且表现出不一致的特征。结果，在给定数据集上有效的模型不能保证在另一个数据集上执行。此外，随着时间的推移，数据模式和特征的时间漂移​​的可能性很高。此外，欺诈数据具有巨大的不平衡。在这项工作中，我们将抽样方法评估为可行的预处理机制，以处理失衡并提出数据驱动的分类器选择策略，以高度不平衡欺诈检测数据集。基于我们的选择策略得出的模型超过了同行模型，同时在更现实的条件下工作，建立了策略的有效性。

信用卡在现代经济体中起着爆炸性的作用。它的受欢迎程度和普遍存在为欺诈行为创造了肥沃的理由，并在跨寄宿范围和瞬时确认的帮助下。尽管交易在增长，但欺诈百分比也在上升，以及一美元欺诈的真实成本。交易的数量，欺诈的独特性和欺诈者的创造力是检测欺诈行为的主要挑战。机器学习，人工智能和大数据的出现为打击欺诈的斗争打开了新的工具。鉴于过去的交易，机器学习算法具有“学习”无限复杂特征的能力，以实时识别欺诈，超过了最佳的人类研究者。但是，欺诈检测算法的发展由于欺诈数据的性质，缺乏基准和标准评估指标的严重不平衡性质而变得挑战和缓慢用于研究的机密交易数据。这项工作调查了典型的欺诈数据集的属性，其可用性，适用于研究用途，同时探索欺诈分布的广泛变化性质。此外，我们展示了人类注释错误与机器分类错误的复合。我们还进行了实验，以确定PCA混淆的影响（作为传播研究和机器学习的敏感交易数据的一种手段）对分类器的算法性能的影响，并表明PCA并未显着降低性能，但应注意使用谨慎适当的主要组件大小（尺寸），以避免过度拟合。

Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malicious) network data classes during the training phase. However, attack data samples are often challenging to collect in most organisations due to security controls preventing the penetration of known malicious traffic to their networks. Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples. The novel one-class classification architecture consists of a histogram-based deep feed-forward classifier to extract useful network data features and use efficient outlier detection. The DOC classifier has been extensively evaluated using two benchmark NIDS datasets. The results demonstrate its superiority over current state-of-the-art one-class classifiers in terms of detection and false positive rates.

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

This is paper for the smooth function approximation by neural networks (NN). Mathematical or physical functions can be replaced by NN models through regression. In this study, we get NNs that generate highly accurate and highly smooth function, which only comprised of a few weight parameters, through discussing a few topics about regression. First, we reinterpret inside of NNs for regression; consequently, we propose a new activation function--integrated sigmoid linear unit (ISLU). Then special charateristics of metadata for regression, which is different from other data like image or sound, is discussed for improving the performance of neural networks. Finally, the one of a simple hierarchical NN that generate models substituting mathematical function is presented, and the new batch concept ``meta-batch" which improves the performance of NN several times more is introduced. The new activation function, meta-batch method, features of numerical data, meta-augmentation with metaparameters, and a structure of NN generating a compact multi-layer perceptron(MLP) are essential in this study.