Machine learning best practice statements have proliferated, but there is a lack of consensus on what the standards should be. For fairness standards in particular, there is little guidance on how fairness might be achieved in practice. Specifically, fairness in errors (both false negatives and false positives) can pose a problem of how to set weights, how to make unavoidable tradeoffs, and how to judge models that present different kinds of errors across racial groups. This paper considers the consequences of having higher rates of false positives for one racial group and higher rates of false negatives for another racial group. The paper examines how different errors in justice settings can present problems for machine learning applications, the limits of computation for resolving tradeoffs, and how solutions might have to be crafted through courageous conversations with leadership, line workers, stakeholders, and impacted communities.

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

Has the NFL put the proper COVID-19 protocols in place to protect players for the 2020 season? Former NFL player Jack Brewer reacts. The Detroit Lions removed Matthew Stafford from the COVID-IR list, saying he received a false-positive test result -- and drawing the ire of the quarterback's wife toward the NFL. The list was created for players who either test positive for COVID-19 or have been in close contact with an infected person. Stafford was listed on it Saturday, but the team said Tuesday his testing sequence for the pre-entry period was: negative, negative, false positive -- then the next three tests were all negative.

To carry out our mathematical study, we have used the standard Decimal Code (R,G,B) to represent the 12 colours of [12], a depiction of which is in Figure 1. The relation between colours and human emotion has been studied for more than a century (e.g., see for instance [1-8]). Even longer ago, colours were commonly associated to emotions in a universal manner that allowed populations to understand quickly the given emotions. Figure 1: A depiction of the 12 colors used in [12]. For example, for centuries in many cultures it has been said that someone "had the blues" [29] or "is feeling In the last decades colours have also been studied in blue" when being down or sad. As explained in [9], the terms of emotional reactions to color hue, saturation, and phrase "feeling blue" comes from deepwater sailing ships: brightness (e.g., [14, 15]). Here, we shall put the two If a ship lost the captain or any of the officers during its approaches together to consider a novel path, where we voyage, then blue flags would be shown, and a blue band let the colour association within our neural network take would be painted along the entire hull when returning to a continuum of colours, hence considering a continuous home port. RGB analysis [30], depicted in Figure 2. Inspired by [10, 11] we consider their data base [12] to analize the correlation between colours and emotions via a deep learning approach. Whilst machine learning techniques have been used before in this direction (e.g.

This article considers "sketched learning," or "compressive learning," an approach to large-scale machine learning where datasets are massively compressed before learning (e.g., clustering, classification, or regression) is performed. In particular, a "sketch" is first constructed by computing carefully chosen nonlinear random features (e.g., random Fourier features) and averaging them over the whole dataset. Parameters are then learned from the sketch, without access to the original dataset. This article surveys the current state-of-the-art in sketched learning, including the main concepts and algorithms, their connections with established signal-processing methods, existing theoretical guarantees---on both information preservation and privacy preservation, and important open problems.

While global cybersecurity spending now surpasses $100 billion annually, 64 percent of enterprises were compromised in 2018, according to a study by the Ponemon Institute. The standard answer is that wily cyber-criminals are employing ever-evolving, increasingly sophisticated attack methods, part of a never-ending game of cat-and-mouse in which they all too often outsmart the good guys. This is undoubtedly true – but the root of the problem is that traditional machine learning-based cybersecurity solutions fail to keep up with the growing sophistication of today's cyber threats, both those that are created by hackers and AI alike. Why does machine learning so often come up short – and how should cybersecurity evolve to meet the scale and complexity of the challenge? There's no question that machine learning has driven significant improvements in cybersecurity.

Confusion matrix (also called Error matrix) is used to analyze how well the Classification Models (like Logistic Regression, Decision Tree Classifier, etc.) performs. Why do we analyze the performance of the models? Analyzing the performance of the models helps us to find and eliminate the bias and variance problem if exist and it also helps us to fine-tune the model so that the model produces more accurate results. Confusion Matrix is usually applied to Binary classification problems but can be extended to Multi-class classification problems as well. Concepts are comprehended better when illustrated with examples so let us consider an example.

Artificial intelligence (AI) and machine learning (ML) have made a paradigm shift in health care which, eventually can be used for decision support and forecasting by exploring the medical data. Recent studies showed that AI and ML can be used to fight against the COVID-19 pandemic. Therefore, the objective of this review study is to summarize the recent AI and ML based studies that have focused to fight against COVID-19 pandemic. From an initial set of 634 articles, a total of 35 articles were finally selected through an extensive inclusion-exclusion process. In our review, we have explored the objectives/aims of the existing studies (i.e., the role of AI/ML in fighting COVID-19 pandemic); context of the study (i.e., study focused to a specific country-context or with a global perspective); type and volume of dataset; methodology, algorithms or techniques adopted in the prediction or diagnosis processes; and mapping the algorithms/techniques with the data type highlighting their prediction/classification accuracy. We particularly focused on the uses of AI/ML in analyzing the pandemic data in order to depict the most recent progress of AI for fighting against COVID-19 and pointed out the potential scope of further research.

We present a review and taxonomy of 200 models from the literature on driver behavior modeling. We begin by introducing a mathematical formulation based on the partially observable stochastic game, which serves as a common framework for comparing and contrasting different driver models. Our taxonomy is constructed around the core modeling tasks of state estimation, intention estimation, trait estimation, and motion prediction, and also discusses the auxiliary tasks of risk estimation, anomaly detection, behavior imitation and microscopic traffic simulation. Existing driver models are categorized based on the specific tasks they address and key attributes of their approach.

In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.