We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks. We implement an innovative AI-based approach to detect, classify and localize underwater events. In this paper, we describe the technology and cognitive AI architecture of the system based on one of the sensor networks, the hydrophone network. We discuss the challenges of installing and using the hydrophone network in a water reservoir where traffic, visitors, and variable water conditions create a complex, varying environment. Our AI solution uses an autoencoder for unsupervised learning of latent encodings for classification and anomaly detection, and time delay estimates for sound localization. Finally, we present the results of experiments carried out in a laboratory pool and the water reservoir and discuss the system's potential.

Person re-identification (Re-ID) aims to match person images across non-overlapping camera views. The majority of Re-ID methods focus on small-scale surveillance systems in which each pedestrian is captured in different camera views of adjacent scenes. However, in large-scale surveillance systems that cover larger areas, it is required to track a pedestrian of interest across distant scenes (e.g., a criminal suspect escapes from one city to another). Since most pedestrians appear in limited local areas, it is difficult to collect training data with cross-camera pairs of the same person. In this work, we study intra-camera supervised person re-identification across distant scenes (ICS-DS Re-ID), which uses cross-camera unpaired data with intra-camera identity labels for training. It is challenging as cross-camera paired data plays a crucial role for learning camera-invariant features in most existing Re-ID methods. To learn camera-invariant representation from cross-camera unpaired training data, we propose a cross-camera feature prediction method to mine cross-camera self supervision information from camera-specific feature distribution by transforming fake cross-camera positive feature pairs and minimize the distances of the fake pairs. Furthermore, we automatically localize and extract local-level feature by a transformer. Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme for mining fine-grained cross-camera self supervision information. Finally, cross-camera self supervision and intra-camera supervision are aggregated in a framework. The experiments are conducted in the ICS-DS setting on Market-SCT, Duke-SCT and MSMT17-SCT datasets. The evaluation results demonstrate the superiority of our method, which gains significant improvements of 15.4 Rank-1 and 22.3 mAP on Market-SCT as compared to the second best method.

With the advancement of technology, Artificial Intelligence starts to live its golden age. We wake up everyday to new and exciting inventions that can be used for the benefit of living things. Throughout the history, human beings are influenced by the nature. We use nature to cope with the problems we encountered by mimicking it. A lot of tools and vehicles are inspired by animals and nature.

The ability to scale machine learning operations (MLOps) at an enterprise is quickly becoming a competitive advantage in the modern economy. When firms started dabbling in ML, only the highest priority use cases were the focus. Businesses are now demanding more from ML practitioners: more intelligent features, delivered faster, and continually maintained over time. An effective MLOps strategy requires a unified platform that can orchestrate and automate complex data processing and ML tasks, and integrates with the latest tooling to best complete those tasks. This post demonstrates the value of using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate an ML pipeline using the popular XGBoost (eXtreme Gradient Boosting) algorithm.

In the previous blog, we discussed a predictive model for house prices using Machine Learning algorithms. In this blog, we are going to discuss the time series forecasting on Zillow economics data using a statistical modeling approach. The project was implemented in September 2019 and forecasting of house prices was done for the next year that is 2020. The code could be reused by changing the span of forecasting that is year for forecasting or duration of forecasting. The results discussed in this blog are for the year 2020.

MCMC methods are a family of algorithms that uses Markov Chains to perform Monte Carlo estimate. The name gives us a hint, that it is composed of two components -- Monte Carlo and Markov Chain. Let us understand them separately and in their combined form. Monte Carlo method derives its name from a Monte Carlo casino in Monaco. It is a technique for sampling from a probability distribution and using those samples to approximate desired quantity. In other words, it uses randomness to estimate some deterministic quantity of interest.

The algorithm uses ideas from Linear Programming, in particular Network Models. Networks models are used, among other things, in logistics to optimise the flow of goods across a network of roads. We can see in the simple figure above that we have 5 nodes with directed arcs (the arrows) between them. Each node has a demand (negative) or supply (positive) value and the arcs have flow and cost values. For instance, the arc 2–4 has a flow of 4 and a cost of $2. Similarly, node 1 supplies 20 units and node 4 requires 5 units.

Hope you all are doing well in this hard time of the Covid era. In this article, we are going to predict the wind speed of the current date and time for any given latitude and longitude coordinates. We'll be using a K-neighbors classifier to build our predicting model. The dataset we are using is available on GitHub here. The first step which I always suggest is to check the python version which you are using.

Bounding the generalization error of a supervised learning algorithm is one of the most important problems in learning theory, and various approaches have been developed. However, existing bounds are often loose and lack of guarantees. As a result, they may fail to characterize the exact generalization ability of a learning algorithm. Our main contribution is an exact characterization of the expected generalization error of the well-known Gibbs algorithm in terms of symmetrized KL information between the input training samples and the output hypothesis. Such a result can be applied to tighten existing expected generalization error bound. Our analysis provides more insight on the fundamental role the symmetrized KL information plays in controlling the generalization error of the Gibbs algorithm.

In this work we study statistical properties of graph-based algorithms for multi-manifold clustering (MMC). In MMC the goal is to retrieve the multi-manifold structure underlying a given Euclidean data set when this one is assumed to be obtained by sampling a distribution on a union of manifolds $\mathcal{M} = \mathcal{M}_1 \cup\dots \cup \mathcal{M}_N$ that may intersect with each other and that may have different dimensions. We investigate sufficient conditions that similarity graphs on data sets must satisfy in order for their corresponding graph Laplacians to capture the right geometric information to solve the MMC problem. Precisely, we provide high probability error bounds for the spectral approximation of a tensorized Laplacian on $\mathcal{M}$ with a suitable graph Laplacian built from the observations; the recovered tensorized Laplacian contains all geometric information of all the individual underlying manifolds. We provide an example of a family of similarity graphs, which we call annular proximity graphs with angle constraints, satisfying these sufficient conditions. We contrast our family of graphs with other constructions in the literature based on the alignment of tangent planes. Extensive numerical experiments expand the insights that our theory provides on the MMC problem.