Robust speech emotion recognition relies on the quality of the speech features. We present speech features enhancement strategy that improves speech emotion recognition. We used the INTERSPEECH 2010 challenge feature-set. We identified subsets from the features set and applied Principle Component Analysis to the subsets. Finally, the features are fused horizontally. The resulting feature set is analyzed using t-distributed neighbour embeddings (t-SNE) before the application of features for emotion recognition. The method is compared with the state-of-the-art methods used in the literature. The empirical evidence is drawn using two well-known datasets: Emotional Speech Dataset (EMO-DB) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for two languages, German and English, respectively. Our method achieved an average recognition gain of 11.5\% for six out of seven emotions for the EMO-DB dataset, and 13.8\% for seven out of eight emotions for the RAVDESS dataset as compared to the baseline study.

Data owners face increasing liability for how the use of their data could harm under-priviliged communities. Stakeholders would like to identify the characteristics of data that lead to algorithms being biased against any particular demographic groups, for example, defined by their race, gender, age, and/or religion. Specifically, we are interested in identifying subsets of the feature space where the ground truth response function from features to observed outcomes differs across demographic groups. To this end, we propose FORESEE, a FORESt of decision trEEs algorithm, which generates a score that captures how likely an individual's response varies with sensitive attributes. Empirically, we find that our approach allows us to identify the individuals who are most likely to be misclassified by several classifiers, including Random Forest, Logistic Regression, Support Vector Machine, and k-Nearest Neighbors. The advantage of our approach is that it allows stakeholders to characterize risky samples that may contribute to discrimination, as well as, use the FORESEE to estimate the risk of upcoming samples.

This paper presents a first end-to-end application of a Quantum Support Vector Machine (QSVM) algorithm for a classification problem in the financial payment industry using the IBM Safer Payments and IBM Quantum Computers via the Qiskit software stack. Based on real card payment data, a thorough comparison is performed to assess the complementary impact brought in by the current state-of-the-art Quantum Machine Learning algorithms with respect to the Classical Approach. A new method to search for best features is explored using the Quantum Support Vector Machine's feature map characteristics. The results are compared using fraud specific key performance indicators: Accuracy, Recall, and False Positive Rate, extracted from analyses based on human expertise (rule decisions), classical machine learning algorithms (Random Forest, XGBoost) and quantum based machine learning algorithms using QSVM. In addition, a hybrid classical-quantum approach is explored by using an ensemble model that combines classical and quantum algorithms to better improve the fraud prevention decision. We found, as expected, that the results highly depend on feature selections and algorithms that are used to select them. The QSVM provides a complementary exploration of the feature space which led to an improved accuracy of the mixed quantum-classical method for fraud detection, on a drastically reduced data set to fit current state of Quantum Hardware.

Maintaining anonymity while communicating using natural language remains a challenge. Standard authorship attribution techniques that analyze candidate authors' writing styles achieve uncomfortably high accuracy even when the number of candidate authors is high. Adversarial stylometry defends against authorship attribution with the goal of preventing unwanted deanonymization. This paper reproduces and replicates experiments in a seminal study of defenses against authorship attribution (Brennan et al., 2012). We are able to successfully reproduce and replicate the original results, although we conclude that the effectiveness of the defenses studied is overstated due to a lack of a control group in the original study. In our replication, we find new evidence suggesting that an entirely automatic method, round-trip translation, merits re-examination as it appears to reduce the effectiveness of established authorship attribution methods.

Purpose; The purpose of this study is to classify glial tumors into grade II, III and IV categories noninvasively by application of machine learning to multi-modal MRI features in comparison with volumetric analysis. Methods; We retrospectively studied 57 glioma patients with pre and postcontrast T1 weighted, T2 weighted, FLAIR images, and ADC maps acquired on a 3T MRI. The tumors were segmented into enhancing and nonenhancing portions, tumor necrosis, cyst and edema using semiautomated segmentation of ITK-SNAP open source tool. We measured total tumor volume, enhancing-nonenhancing tumor, edema, necrosis volume and the ratios to the total tumor volume. Training of a support vector machine (SVM) classifier and artificial neural network (ANN) was performed with labeled data designed to answer the question of interest. Specificity, sensitivity, and AUC of the predictions were computed by means of ROC analysis. Differences in continuous measures between groups were assessed by using Kruskall Wallis, with post hoc Dunn correction for multiple comparisons. Results; When we compared the volume ratios between groups, there was statistically significant difference between grade IV and grade II-III glial tumors. Edema and tumor necrosis volume ratios for grade IV glial tumors were higher than that of grade II and III. Volumetric ratio analysis could not distinguish grade II and III tumors successfully. However, SVM and ANN correctly classified each group with accuracies up to 98% and 96%. Conclusion; Application of machine learning methods to MRI features can be used to classify brain tumors noninvasively and more readily in clinical settings.

The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.

In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum computer, and QBoost on a quantum annealer. The quantum vision systems are benchmarked for an unbalanced dataset of images where the aim is to detect defects in manufactured car pieces. We see that the quantum algorithms outperform their classical counterparts in several ways, with QBoost allowing for larger problems to be analyzed with present-day quantum annealers. Data preprocessing, including dimensionality reduction and contrast enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To the best of our knowledge, this is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.

Automatic identification of emotions expressed in Twitter data has a wide range of applications. We create a well-balanced dataset by adding a neutral class to a benchmark dataset consisting of four emotions: fear, sadness, joy, and anger. On this extended dataset, we investigate the use of Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT) for emotion recognition. We propose a novel ensemble model by combining the two BERT and SVM models. Experiments show that the proposed model achieves a state-of-the-art accuracy of 0.91 on emotion recognition in tweets.

Objective: Classifier transfers usually come with dataset shifts. To overcome them, online strategies have to be applied. For practical applications, limitations in the computational resources for the adaptation of batch learning algorithms, like the SVM, have to be considered. Approach: We review and compare several strategies for online learning with SVMs. We focus on data selection strategies which limit the size of the stored training data [...] Main Results: For different data shifts, different criteria are appropriate. For the synthetic data, adding all samples to the pool of considered samples performs often significantly worse than other criteria. Especially, adding only misclassified samples performed astoundingly well. Here, balancing criteria were very important when the other criteria were not well chosen. For the transfer setups, the results show that the best strategy depends on the intensity of the drift during the transfer. Adding all and removing the oldest samples results in the best performance, whereas for smaller drifts, it can be sufficient to only add potential new support vectors of the SVM which reduces processing resources. Significance: For BCIs based on EEG models, trained on data from a calibration session, a previous recording session, or even from a recording session with one or several other subjects, are used. This transfer of the learned model usually decreases the performance and can therefore benefit from online learning which adapts the classifier like the established SVM. We show that by using the right combination of data selection criteria, it is possible to adapt the classifier and largely increase the performance. Furthermore, in some cases it is possible to speed up the processing and save computational by updating with a subset of special samples and keeping a small subset of samples for training the classifier.

Free Coupon Discount - Ensemble Machine Learning in Python: Random Forest, AdaBoost, Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python Created by Lazy Programmer Inc. Students also bought Unsupervised Deep Learning in Python Machine Learning and AI: Support Vector Machines in Python Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Deep Learning Prerequisites: Linear Regression in Python Preview this Udemy Course GET COUPON CODE Description In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.