随着网络攻击和网络间谍活动的增长，如今需要更好，更强大的入侵检测系统（IDS）的需求更加有必要。 ID的基本任务是在检测Internet的攻击方面充当第一道防线。随着入侵者的入侵策略变得越来越复杂且难以检测，研究人员已经开始应用新颖的机器学习（ML）技术来有效地检测入侵者，从而保留互联网用户对整个互联网网络安全的信息和整体信任。在过去的十年中，基于ML和深度学习（DL）架构的侵入检测技术的爆炸激增，这些架构在各种基于网络安全的数据集上，例如DARPA，KDDCUP'99，NSL-KDD，CAIDA，CAIDA，CTU--- 13，UNSW-NB15。在这项研究中，我们回顾了当代文献，并提供了对不同类型的入侵检测技术的全面调查，该技术将支持向量机（SVMS）算法作为分类器。我们仅专注于在网络安全中对两个最广泛使用的数据集进行评估的研究，即KDDCUP'99和NSL-KDD数据集。我们提供了每种方法的摘要，确定了SVMS分类器的作用以及研究中涉及的所有其他算法。此外，我们以表格形式对每种方法进行了批判性综述，突出了所调查的每种方法的性能指标，优势和局限性。

随着物联网，AI和ML/DL算法的出现，数据驱动的医疗应用已成为一种有前途的工具，用于从医学数据设计可靠且可扩展的诊断和预后模型。近年来，这引起了从学术界到工业的广泛关注。这无疑改善了医疗保健提供的质量。但是，由于这些基于AI的医疗应用程序在满足严格的安全性，隐私和服务标准（例如低延迟）方面的困难，因此仍然采用较差。此外，医疗数据通常是分散的和私人的，这使得在人群之间产生强大的结果具有挑战性。联邦学习（FL）的最新发展使得以分布式方式训练复杂的机器学习模型成为可能。因此，FL已成为一个积极的研究领域，尤其是以分散的方式处理网络边缘的医疗数据，以保护隐私和安全问题。为此，本次调查论文重点介绍了数据共享是重大负担的医疗应用中FL技术的当前和未来。它还审查并讨论了当前的研究趋势及其设计可靠和可扩展模型的结果。我们概述了FL将军的统计问题，设备挑战，安全性，隐私问题及其在医疗领域的潜力。此外，我们的研究还集中在医疗应用上，我们重点介绍了全球癌症的负担以及有效利用FL来开发计算机辅助诊断工具来解决这些诊断工具。我们希望这篇评论是一个检查站，以彻底的方式阐明现有的最新最新作品，并为该领域提供开放的问题和未来的研究指示。

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}

The devastation caused by the coronavirus pandemic makes it imperative to design automated techniques for a fast and accurate detection. We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling Attention-based Multi-scaled Convolution network (EAMC), employing Leave-One-Patient-Out (LOPO) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The Attention module combines contextual with local information, at multiple scales, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of EAMC, even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. Its clinical significance lies in its unprecedented scope in providing low-cost decision-making for patients lacking specialized healthcare at remote locations.

Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.

Candidate axiom scoring is the task of assessing the acceptability of a candidate axiom against the evidence provided by known facts or data. The ability to score candidate axioms reliably is required for automated schema or ontology induction, but it can also be valuable for ontology and/or knowledge graph validation. Accurate axiom scoring heuristics are often computationally expensive, which is an issue if you wish to use them in iterative search techniques like level-wise generate-and-test or evolutionary algorithms, which require scoring a large number of candidate axioms. We address the problem of developing a predictive model as a substitute for reasoning that predicts the possibility score of candidate class axioms and is quick enough to be employed in such situations. We use a semantic similarity measure taken from an ontology's subsumption structure for this purpose. We show that the approach provided in this work can accurately learn the possibility scores of candidate OWL class axioms and that it can do so for a variety of OWL class axioms.

The rapid growth of machine translation (MT) systems has necessitated comprehensive studies to meta-evaluate evaluation metrics being used, which enables a better selection of metrics that best reflect MT quality. Unfortunately, most of the research focuses on high-resource languages, mainly English, the observations for which may not always apply to other languages. Indian languages, having over a billion speakers, are linguistically different from English, and to date, there has not been a systematic study of evaluating MT systems from English into Indian languages. In this paper, we fill this gap by creating an MQM dataset consisting of 7000 fine-grained annotations, spanning 5 Indian languages and 7 MT systems, and use it to establish correlations between annotator scores and scores obtained using existing automatic metrics. Our results show that pre-trained metrics, such as COMET, have the highest correlations with annotator scores. Additionally, we find that the metrics do not adequately capture fluency-based errors in Indian languages, and there is a need to develop metrics focused on Indian languages. We hope that our dataset and analysis will help promote further research in this area.

The intersection of ground reaction forces in a small, point-like area above the center of mass has been observed in computer simulation models and human walking experiments. This intersection point is often called a virtual pivot point (VPP). With the VPP observed so ubiquitously, it is commonly assumed to provide postural stability for bipedal walking. In this study, we challenge this assumption by questioning if walking without a VPP is possible. Deriving gaits with a neuromuscular reflex model through multi-stage optimization, we found stable walking patterns that show no signs of the VPP-typical intersection of ground reaction forces. We, therefore, conclude that a VPP is not necessary for upright, stable walking. The non-VPP gaits found are stable and successfully rejected step-down perturbations, which indicates that a VPP is not primarily responsible for locomotion robustness or postural stability. However, a collision-based analysis indicates that non-VPP gaits increased the potential for collisions between the vectors of the center of mass velocity and ground reaction forces during walking, suggesting an increased mechanical cost of transport. Although our computer simulation results have yet to be confirmed through experimental studies, they already strongly challenge the existing explanation of the VPP's function and provide an alternative explanation.