Withdrawal ruptures in adolescents with borderline personality disorder psychotherapy are marked by increased speech pauses-can minimal responses be automatically detected?

Alliance ruptures of the withdrawal type are prevalent in adolescents with borderline personality disorder (BPD). Longer speech pauses are negatively perceived by these patients. Safran and Muran's rupture model is promising but its application is very work intensive. This workload makes research costly and limits clinical usage. We hypothesised that pauses can be used to automatically detect one of the markers of the rupture model i.e. the minimal response marker. Additionally, the association of withdrawal ruptures with pauses was investigated. A total of 516 ruptures occurring in 242 psychotherapy sessions collected in 22 psychotherapies of adolescent patients with BPD and subthreshold BPD were investigated. Trained observers detected ruptures based on video and audio recordings. In contrast, pauses were automatically marked in the audio-recordings of the psychotherapy sessions and automatic speaker diarisation was used to determine the speaker-switching patterns in which the pauses occur. A random forest classifier detected time frames in which ruptures with the minimal response marker occurred based on the quantity of pauses. Performance was very good with an area under the ROC curve of 0.89. Pauses which were both preceded and followed by therapist speech were the most important predictors for minimal response ruptures. Research costs can be reduced by using machine learning techniques instead of manual rating for rupture detection. In combination with other video and audio derived features like movement analysis or automatic facial emotion detection, more complete rupture detection might be possible in the future. These innovative machine learning techniques help to narrow down the mechanisms of change of psychotherapy, here specifically of the therapeutic alliance. They might also be used to technologically augment psychotherapy training and supervision

Detecting Concept Drift With Neural Network Model Uncertainty

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios-like the ones covered in this work-true labels are scarce, and their acquisition is expensive. Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels. Our approach is based on the uncertainty estimates provided by a deep neural network in combination with Monte Carlo Dropout. Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model rather than detecting change on the input data only (which can lead to unnecessary retrainings). We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks

Aplicación de las técnicas de Machine Learning para la detección en imágenes de monedas falsas y verdaderas de cinco soles

Conseguir desarrollar un modelo con técnicas de Machine Learning para la detección por imágenes en monedas falsas y verdaderas de cinco soles Peruanos El tipo de investigación en este proyecto definió como básico tecnológico con un nivel experimental y un diseño pre-experimental al usar como métrica porcentual derivada de la ma- triz de confusión. La población se constituyó de un total de 496 imágenes de monedas de cinco soles entre verdaderas y falsas teniendo las cuales pasaran por la técnica de Data Augmentation para tener 800 imágenes por cada categoría expedidas en el modelo 2010-2015 y como instrumento de recolección de datos utilizaremos una cámara fotografía. Como conclusión principal tenemos que el modelo “Final_Model” se desarrolló usando las técnicas de Machine Learning de manera teórica y práctica que conllevo a cumplir nuestro objetivo principal en esta investigación que es aplicar las técnicas de Machine Learning en la detección de monedas falsas y verdaderas de cinco soles Peruanos satisfactoriamente respondiendo al objetivo principal de la investigación.This research focuses on the application of Machine Learning techniques for the development of a model that allows the detection by images of false and true coins of five Peruvian soles. Since the invention of currency, counterfeiting was also born. It is necessary to make proposals for change in the aspects concerning the means of security in physical means of payment to protect the economic, social and political level. For its part, the field of Machine Learning has grown more intensely since 2009, being able to apply to more branches of study, our country is late in the use of Machine Learning techniques since we find a lack of studies and development of tools that apply Machine Learning being a problem to reach a solution in detection of false and true coins in the REPUBLIC OF PERU. Therefore, we propose the application of Machine Learning techniques to contribute to a future solution to this latent problem. This research proposed the construction of a model using the Transfer Learning technique to join a pre-trained model and a personalized head model that was trained with images of true and false coins from the year 2010-2015. Analyzing the learning curve of the model and using the confusion matrix, the average error of the predictions was obtained with an approximate error of 20% in a population of 1600 photographic samples between false and true coins.Desarrollo de aplicaciones usando inteligencia artificia

Handling Concept Drift for Predictions in Business Process Mining

Predictive services nowadays play an important role across all business sectors. However, deployed machine learning models are challenged by changing data streams over time which is described as concept drift. Prediction quality of models can be largely influenced by this phenomenon. Therefore, concept drift is usually handled by retraining of the model. However, current research lacks a recommendation which data should be selected for the retraining of the machine learning model. Therefore, we systematically analyze different data selection strategies in this work. Subsequently, we instantiate our findings on a use case in process mining which is strongly affected by concept drift. We can show that we can improve accuracy from 0.5400 to 0.7010 with concept drift handling. Furthermore, we depict the effects of the different data selection strategies

Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications. However, machine learning models are known to lack robustness against inputs crafted by an adversary. So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model. Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well. We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors. We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used. Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure