Most classification models treat all misclassifications equally. However, different classes may be related, and these hierarchical relationships must be considered in some classification problems. These problems can be addressed by using hierarchical information during training. Unfortunately, this information is not available for all datasets. Many classification-based metric learning methods use class representatives in embedding space to represent different classes. The relationships among the learned class representatives can then be used to estimate class hierarchical structures. If we have a predefined class hierarchy, the learned class representatives can be assessed to determine whether the metric learning model learned semantic distances that match our prior knowledge. In this work, we train a softmax classifier and three metric learning models with several training options on benchmark and real-world datasets. In addition to the standard classification accuracy, we evaluate the hierarchical inference performance by inspecting learned class representatives and the hierarchy-informed performance, i.e., the classification performance, and the metric learning performance by considering predefined hierarchical structures. Furthermore, we investigate how the considered measures are affected by various models and training options. When our proposed ProxyDR model is trained without using predefined hierarchical structures, the hierarchical inference performance is significantly better than that of the popular NormFace model. Additionally, our model enhances some hierarchy-informed performance measures under the same training options. We also found that convolutional neural networks (CNNs) with random weights correspond to the predefined hierarchies better than random chance.

The intersection of social media, low-cost trading platforms, and naive investors has created an ideal situation for information-based market manipulations, especially pump&dumps. Manipulators accumulate small-cap stocks, disseminate false information on social media to inflate their price, and sell at the peak. We collect a dataset of stocks whose price and volume profiles have the characteristic shape of a pump&dump, and social media posts for those same stocks that match the timing of the initial price rises. From these we build predictive models for pump&dump events based on the language used in the social media posts. There are multiple difficulties: not every post will cause the intended market reaction, some pump&dump events may be triggered by posts in other forums, and there may be accidental confluences of post timing and market movements. Nevertheless, our best model achieves a prediction accuracy of 85% and an F1-score of 62%. Such a tool can provide early warning to investors and regulators that a pump&dump may be underway.

Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach's average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.

Ransomware is a rapidly evolving type of malware designed to encrypt user files on a device, making them inaccessible in order to exact a ransom. Ransomware attacks resulted in billions of dollars in damages in recent years and are expected to cause hundreds of billions more in the next decade. With current state-of-the-art process-based detectors being heavily susceptible to evasion attacks, no comprehensive solution to this problem is available today. This paper presents Minerva, a new approach to ransomware detection. Unlike current methods focused on identifying ransomware based on process-level behavioral modeling, Minerva detects ransomware by building behavioral profiles of files based on all the operations they receive in a time window. Minerva addresses some of the critical challenges associated with process-based approaches, specifically their vulnerability to complex evasion attacks. Our evaluation of Minerva demonstrates its effectiveness in detecting ransomware attacks, including those that are able to bypass existing defenses. Our results show that Minerva identifies ransomware activity with an average accuracy of 99.45% and an average recall of 99.66%, with 99.97% of ransomware detected within 1 second.

One of the most important aspects of data science is building trust. This is especially true when you're working with machine learning and AI technologies, which are new and unfamiliar to many people. When something goes wrong, what do you tell your customer? What do they think will happen next? With explainable AI, you can provide answers that prove your product's legitimacy.

Mental disorders such as depression and suicidal ideation are hazardous, affecting more than 300 million people over the world. However, on social media, mental disorder symptoms can be observed, and automated approaches are increasingly capable of detecting them. The considerable number of social media users and the tremendous quantity of user-generated data on social platforms provide a unique opportunity for researchers to distinguish patterns that correlate with mental status. This research offers a roadmap for analysis, where mental state detection can be based on machine learning techniques. We describe the common approaches for predicting and identifying the disorder using user-generated content. This research is organized according to the data collection, feature extraction, and prediction algorithms. Furthermore, we review several recent studies conducted to explore different features of candidate profiles and their analytical methods. Following, we debate various aspects of the development of experimental auto-detection frameworks for identifying users who suffer from disorders, and we conclude with a discussion of future trends. The introduced methods can help complement screening procedures, identify at-risk people through social media monitoring on a large scale, and make disorders easier to treat in the future.

Uncertainty in perception, actuation, and the environment often require multiple attempts for a robotic task to be successful. We study a class of problems providing (1) low-entropy indicators of terminal success / failure, and (2) unreliable (high-entropy) data to predict the final outcome of an ongoing task. Examples include a robot trying to connect with a charging station, parallel parking, or assembling a tightly-fitting part. The ability to restart after predicting failure early, versus simply running to failure, can significantly decrease the makespan, that is, the total time to completion, with the drawback of potentially short-cutting an otherwise successful operation. Assuming task running times to be Poisson distributed, and using a Markov Jump process to capture the dynamics of the underlying Markov Decision Process, we derive a closed form solution that predicts makespan based on the confusion matrix of the failure predictor. This allows the robot to learn failure prediction in a production environment, and only adopt a preemptive policy when it actually saves time. We demonstrate this approach using a robotic peg-in-hole assembly problem using a real robotic system. Failures are predicted by a dilated convolutional network based on force-torque data, showing an average makespan reduction from 101s to 81s (N=120, p<0.05). We posit that the proposed algorithm generalizes to any robotic behavior with an unambiguous terminal reward, with wide ranging applications on how robots can learn and improve their behaviors in the wild.

STEALTH is a method for using some AI-generated model, without suffering from malicious attacks (i.e. lying) or associated unfairness issues. After recursively bi-clustering the data, STEALTH system asks the AI model a limited number of queries about class labels. STEALTH asks so few queries (1 per data cluster) that malicious algorithms (a) cannot detect its operation, nor (b) know when to lie.

Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics that focus on distinct and complementary aspects of forecast performance: The reliability diagram addresses calibration, the receiver operating characteristic (ROC) curve diagnoses discrimination ability, and the Murphy diagram visualizes overall predictive performance and value. A Murphy curve shows a forecast's mean elementary scores, including the widely used misclassification rate, and the area under a Murphy curve equals the mean Brier score. For a calibrated forecast, the reliability curve lies on the diagonal, and for competing calibrated forecasts, the ROC and Murphy curves share the same number of crossing points. We invoke the recently developed CORP (Consistent, Optimally binned, Reproducible, and Pool-Adjacent-Violators (PAV) algorithm based) approach to craft reliability diagrams and decompose a mean score into miscalibration (MCB), discrimination (DSC), and uncertainty (UNC) components. Plots of the DSC measure of discrimination ability versus the calibration metric MCB visualize classifier performance across multiple competitors. The proposed tools are illustrated in empirical examples from astrophysics, economics, and social science.

Based on a report by the International Diabetes Federation, in 2021, 537 million adults globally were suffering from diabetes causing 6.7 million deaths [3]. Furthermore, the number of diabetics is projected to reach 643 million by 2030 and 783 million by 2045 [3]. Diabetes in an individual prevails due to a dynamic interaction between different risk factors such as sleep duration, alcohol consumption, dyslipidemia, physical inactivity, serum uric acid, obesity, hypertension, cardiovascular disease, family history of diabetes, ethnicity, depression, age, and gender [4]. If not treated at an early stage, diabetes can lead to severe complications [5]. The use of machine learning has thus gained wide attention for the prediction of diabetes based on risk factors data [6-13] in context of smart healthcare [14,15].