Unlike previous work that mostly focused on the algorithmic design of adversarial examples in terms of improving the success rate as an attacker, in this work we show an interpretation of such patches that can prevent the state-of-the-art face detectors from detecting the real faces. W e investigate a phenomenon: patches designed to suppress real face detection appear face-like. This phenomenon holds generally across different initialization, locations, scales of patches, backbones, and state-of-the-art face detection frameworks. W e propose new optimization-based approaches to automatic design of universal adversarial patches for varying goals of the attack, including scenarios in which true positives are suppressed without introducing false positives. Our proposed algorithms perform well on real-world datasets, deceiving state-of-the-art face detectors in terms of multiple precision/recall metrics and transferring between different detection frameworks. 1. Introduction Adversarial examples are a central object of study in computer vision [33], machine learning [39, 23], security [26], and other domains [13]. In computer vision and machine learning, study of adversarial examples serves as evidences of substantial discrepancy between human vision system and machine perception mechanism [30, 25, 2, 9]. In security, adversarial examples have raised major concerns on the vulnerability of machine learning systems to malicious attacks. The problem can be stated as modifying an image, subject to some constraints, so that learning system's response is drastically altered, e.g., changing the classifier or detector output from correct to incorrect. The constraints either come in the human-imperceptible form Equal contribution.

This work aims to examine one of the cornerstone problems of Musical Instrument Recognition, in particular instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen. The data includes music obtained from various decades in the last century, thus having a wide variety in audio quality. We have presented a very concise summary of past work in this domain. Having implemented various supervised learning algorithms for this classification task, SVM classifier has outperformed the other state-of-the-art models with an accuracy of 79%. The classifier had a major challenge distinguishing between flute and organ. We also implemented Unsupervised techniques out of which Hierarchical Clustering has performed well. We have included most of the code (jupyter notebook) for easy reproducibility.

The last decade has seen rapid advancements in machine learning techniques enabling automation and predictions in scales never imagined before. This further prompts researchers and engineers to conceive new applications for these beautiful techniques. It wasn't long before machine learning techniques were used in reinforcing network security systems. The most common risk to a network's security is an intrusion such as brute force, denial of service, or even an infiltration from within a network. With the changing patterns in network behavior, it is necessary to switch to a dynamic approach to detect and prevent such intrusions.

Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been subject to a fundamental restriction, in that it applies to dichotomous (yes or no) outcomes only. We introduce ROC movies and universal ROC (UROC) curves that apply to just any ordinal or real-valued outcome, along with a new, asymmetric coefficient of predictive ability (CPA) measure. CPA equals the area under the UROC curve and admits appealing interpretations in terms of probabilities and rank based covariances. ROC movies, UROC curves and CPA nest and generalize the classical ROC curve and AUC, and are bound to supersede them in a wealth of applications.

In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category. Eliminating some groups of variables can expedite the process of data acquisition and avoid over-fitting. Researchers have used the group lasso to ensure group sparsity in linear models and have extended it to create compact neural networks in meta-learning. Different from previous studies, we use multi-layer non-linear neural networks to find sparse groups for input variables. We propose a new loss function to regularize parameters for grouped input variables, design a new optimization algorithm for this loss function, and test these methods in three real-world settings. We achieve group sparsity for three datasets, maintaining satisfying results while excluding one nucleotide position from an RNA splicing experiment, excluding 89.9% of stimuli from an eye-tracking experiment, and excluding 60% of image rows from an experiment on the MNIST dataset.

Epilepsy is a n important public health issue. An appropriate epileptiform discharge pattern detectio n of this neurological disease is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike - and - wave discharge pattern dete ction based on Kendall's Tau - b c oefficient. The proposed approach is demonstrated on a real data set containing spike - and - wave discharge signals, where our performance is evaluated in terms of high Specificity, rule in (SpPIn) with 94% for patient - specific spike - and - wave discharge detection and 83% for a general spike - and - wave discharge detection. Key words: Spike - and - wave discharge; Kendall's Tau - b c oefficient; Electroencephalography ( EEG); Epilepsy; high Specificity, rule in ( SpPIn) Introduction Electroencephalography (EEG) is widely used to record the electrical activity of the brain in neurological health centers.

A shorter version of this article was originally published by Scientific American. With articles like these, the press will have you believe that machine learning can reliably predict whether you're gay, whether you'll develop psychosis, whether you'll have a heart attack, and whether you're a criminal – as well as other ambitious predictions such as when you'll die and whether your unpublished book will be a bestseller. Machine learning can't confidently tell such things about each individual. In most cases, these things are simply too difficult to predict with certainty. Researchers report high "accuracy," but then later reveal – buried within the details of a technical paper – that they were actually misusing the word "accuracy" to mean another measure of performance related to accuracy but in actuality not nearly as impressive.

Fueled by mobster movies and international espionage thrillers, the phrase has a mysterious, exciting edge to it. But as is often the case, the truth is far less appealing than the glitzy Hollywood version. In reality, money laundering is an activity that traps 40.3 million people in modern slavery, fuels political unrest, and finances terrorism across the globe. Considering the consequences, it's no wonder governments enact AML regulations. And just as money laundering crime grows more sophisticated, so too do the regulations. These regulations have honorable and important intentions, but there's no denying the ever-evolving compliance headaches they create for financial institutions.

The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact computational processes are missed if we do not consider the lifecycle stages upstream from model training and deployment. Yet, very little has been done to date to provide system-level support to data scientists who wish to develop and deploy responsible machine learning methods. We aim to fill this gap and present FairPrep, a design and evaluation framework for fairness-enhancing interventions. FairPrep is based on a developer-centered design, and helps data scientists follow best practices in software engineering and machine learning. As part of our contribution, we identify shortcomings in existing empirical studies for analyzing fairness-enhancing interventions. We then show how FairPrep can be used to measure the impact of sound best practices, such as hyperparameter tuning and feature scaling. In particular, our results suggest that the high variability of the outcomes of fairness-enhancing interventions observed in previous studies is often an artifact of a lack of hyperparameter tuning. Further, we show that the choice of a data cleaning method can impact the effectiveness of fairness-enhancing interventions.

Free-riders in Federated Learning: Attacks and Defenses Jierui Lin, Min Du, and Jian Liu University of California, Berkeley Abstract--Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model. Therefore, a client who does not have any local data has the incentive to construct local gradient updates in order to deceive for rewards. In this paper, we are the first to propose the notion of free rider attacks, to explore possible ways that an attacker may construct gradient updates, without any local training data. Furthermore, we explore possible defenses that could detect the proposed attacks, and propose a new high dimensional detection method called STD-DAGMM, which particularly works well for anomaly detection of model parameters. We extend the attacks and defenses to consider more free riders as well as differential privacy, which sheds light on and calls for future research in this field. I NTRODUCTION F EDERA TED learning [1], [2], [3] has been proposed to facilitate a joint model training leveraging data from multiple clients, where the training process is coordinated by a parameter server. In the whole process, clients' data stay local, and only model parameters are communicated among clients through the parameter server. A typical training iteration works as follows. First, the parameter server sends the newest global model to each client. Then, each client locally updates the model using local data and reports updated gradients to the parameter server. Finally, the server performs model aggregation on all submitted local updates to form a new global model, which has better performance than models trained using any single client's data. Compared with an alternative approach which simply collects all data from the clients and trains a model on those data, federated learning is able to save the communication overhead by only transmitting model parameters, as well as protect privacy since all data stay local.