Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have not fully addressed the unique challenges in UV detection such as the scarcity of labeled UVs and the diverse urban patterns in different regions. To this end, we first build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area. Finally, we conduct extensive experiments in three cities to demonstrate the effectiveness of our approach.

Abstract: Clustering is an effective technique in data mining to group a set of objects in terms of some attributes. However, most of existing K-Means based clustering algorithms cannot deal with outliers well and are difficult to efficiently solve the problem embedded the L0-norm constraint. To address the above issues and improve the performance of clustering significantly, we propose a novel clustering algorithm, named REFCMFS, which develops a L2,1-norm robust loss as the data-driven item and imposes a L0-norm constraint on the membership matrix to make the model more robust and sparse flexibly. In particular, REFCMFS designs a new way to simplify and solve the L0-norm constraint without any approximate transformation by absorbing 0 into the objective function through a ranking function. These improvements not only make REFCMFS efficiently obtain more promising performance but also provide a new tractable and skillful optimization method to solve the problem embedded the L0-norm constraint.

Bigdata a buzzword itself suggest how big and voluminous your data is. To accommodate those data you require a huge storage.In recent times, big data has acquired almost every sector of the world. Even the current market trends are of Bigdata and analytics. BigData is just like an ocean in which you have many areas to learn and earn from it. Python programming is a general-purpose programming language that is open source, flexible, robust and simple.

Released in 2018, BERT has quickly become one of the most popular models for natural language processing, with the original paper accumulating tens of thousands of citations. Its success recipe lies in a simple training method called masked language modeling. Randomly mask 15% of the words in a text, and then ask the model to recover these based on the surrounding context. This simple idea, scaled on billions of words, allowed BERT to develop a deep understanding of human language, reusable for other tasks such as question answering, text summarization, classification of text documents, etc. Given the impressive success of BERT for written language, it is no surprise that researchers are attempting to apply the same recipe to other modalities of language, like human speech.

Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the most popular technique for dimensionality reduction in machine learning is Principal Component Analysis, or PCA for short. This is a technique that comes from the field of linear algebra and can be used as a data preparation technique to create a projection of a dataset prior to fitting a model. In this tutorial, you will discover how to use PCA for dimensionality reduction when developing predictive models.

This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum -- a well studied baseline for nonlinear model identification and control with continuous states and control inputs -- measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties.

Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently turned out to be a sufficient condition for domain generalization. To this end, our method generates an ensemble of the output logits from training data with the same label but from different domains and then penalizes each output for the mismatch with the ensemble. Also, we present a de-stylization technique that standardizes features to encourage the model to produce style-consistent predictions even in an arbitrary target domain. Our method greatly improves generalization capability in public benchmarks for cross-domain image classification, cross-dataset person re-ID, and cross-dataset semantic segmentation. Moreover, we show that models learned by our method are robust against adversarial attacks and image corruptions.

We study the problem of learning a linear system model from the obse rvations of M clients. The catch: Each agent is observing data from a different dynamical system. This work addresses the question of how multiple systems collaboratively learn dynamical models in the presence of heterogeneity. We pose this problem as a federa ted learning problem and characterize the tension between achievable performance an d system heterogeneity. Furthermore, we provide a sample complexity result that obtains a c onstant factor improvement over the single agent setting. Finally, we describe a meta federated learning algorithm, FedSysID, that leverages existing federated algorithms at the client level.

Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid dynamics (CFD) studies in resource constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography has been seen as a suitable velocity acquisition modality due to its higher availability and safety. This study aimed to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach for obtaining boundary conditions (BCs) from Doppler Echocardiography images, for haemodynamic modeling using CFD. Methods- Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. With the key feature of the approach being the use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of the CFD model. The key input variable for the ML model was the patients heart rate as this was the parameter that varied in time across the measured vessels within the study. ANSYS Fluent was used for the CFD component of the study whilst the scikit-learn python library was used for the ML component. Results- We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations were compared to the measured maximum coarctation velocity obtained from the patient whose geometry is used within the study. Of the 5 ML models used to obtain BCs the top model was within 5\% of the measured maximum coarctation velocity. Conclusion- The framework demonstrated that it was capable of taking variations of the patients heart rate between measurements into account. Thus, enabling the calculation of BCs that were physiologically realistic when the heart rate was scaled across each vessel whilst providing a reasonably accurate solution.

Cyberspace occupies a large portion of people's life in the age of modern technology, and while there are those who utilize it for good, there are also those who do not. Malware is an application whose construction was not motivated by a benign goal and it can harm, steal, or even alter personal information and secure applications and software. Thus, there are numerous techniques to avoid malware, one of which is to develop samples of malware so that the system can be updated with the growing number of malwares, allowing it to recognize when malwares attempt to enter. The Generative Adversarial Network (GAN) sampling technique has been used in this study to generate new malware samples. GANs have multiple variants, and in order to determine which variant is optimal for a given dataset sample, their parameters must be modified. This study employs Optuna, an autonomous hyperparameter tuning algorithm, to determine the optimal settings for the dataset under consideration. In this study, the architecture of the Optuna Optimized GAN (OOG) method is shown, along with scores of 98.06%, 99.00%, 97.23%, and 98.04% for accuracy, precision, recall and f1 score respectively. After tweaking the hyperparameters of five supervised boosting algorithms, XGBoost, LightGBM, CatBoost, Extra Trees Classifier, and Gradient Boosting Classifier, the methodology of this paper additionally employs the weighted ensemble technique to acquire this result. In addition to comparing existing efforts in this domain, the study demonstrates how promising GAN is in comparison to other sampling techniques such as SMOTE.