Data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind-spots. There is a need to improve the drift detection accuracy for histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model.

This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules.

A data scientist is responsible for extracting, pre-procession, manipulating and generating predictions out of data. But to do so, a data scientist requires a variety of statistical tools and programming languages. So, to make the work of every data scientist easy, today we are going to share with you the 10 most used data science tools. These tools are the best to be used by the data scientist to carry out their data operations. You will understand the key features of the tool, benefits these tools provide you with as well as their comparisons.

Blue whales swim to the surface to feed on krill as it helps them to conserve energy, according to a new study that involved amazing drone footage of the mammals. Experts from Oregon State University found that feeding on the ocean's surface plays an important role in the hunt for food among New Zealand blue whales. Blue whales are the largest mammals on Earth and have to carefully balance the cost of energy they get from food with the cost of energy used in getting the food. Researchers say the marine mammals forage for krill in areas where they are densely packed and found near the surface of the water to cut their dive time. The Oregon team found that the blue whales do this to conserve on the energetic costs of feeding such as diving, holding their breath or opening their mouths.

Women are underrepresented in the fields of science, technology, engineering, and mathematics (STEM) in most countries, including Germany and the U.S.29,32 This was demonstrated in several surveys investigating the proportion of women in the STEM fields for specific populations. Some of these studies, for example, investigated the number of enrolled students10,30 or the percentage of female professors at universities. Other studies analyzed the disparities in research funding.23 Nearly all these surveys selected a particular population of women in consideration of their university degree or their nationality.11,34 Like many other studies investigating the gender gap and its reasons in science, these surveys are usually based on data records from several kinds of registrations or enrollments, for example, the enrollment as student or doctoral student, the registration of finished doctoral theses or the membership as professor in a certain country.1,14,16,28 However, researchers at the postdoctoral level or industrial researchers are often not registered and unfortunately drop out of the surveys.

This work presents a Long Short-Term Memory (LSTM) network for forecasting a monthly electricity demand time series with a one-year horizon. The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition. Pattern representation simplifies the complex nonlinear and nonstationary time series, filtering out the trend and equalizing variance. Two types of patterns are defined: x-pattern and y-pattern. The former requires additional forecasting for the coding variables. The latter determines the coding variables from the process history. A hybrid approach based on x-patterns turned out to be more accurate than the standard LSTM approach based on a raw time series. In this combined approach an x-pattern is forecasted using a sequence-to-sequence LSTM network and the coding variables are forecasted using exponential smoothing. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness to classical models such as ARIMA and exponential smoothing as well as the MLP neural network model.

Information retrieval concerns finding documents that are most relevant for a given query, and is a canonical real-world use case for machine learning [Manning et al., 2008]. The simplest incarnation of retrieval models involves learning a real-valued scoring function that ranks, for each example, the set of possible labels it may be matched to. A core challenge is scalability: there may be billions of examples (e.g., user queries) and labels (e.g., videos in a recommendation system), each of whose scores naïvely needs to be updated at every training iteration. Effective means of addressing both problems have been widely studied [Mikolov et al., 2013, Jean et al., 2015, Reddi et al., 2019]. A distinct challenge is heterogeneity: the distribution over examples is often a mixture of diverse subpopulations (e.g., queries may arise from geographically disparate user bases). Naïve training on such data may lead to models that perform disproportionately well on one subpopulation at the expense of others; e.g., if queries originate from multiple countries, the retrieval model may only perform well on queries from the dominant country. Such behaviour is clearly undesirable.

Kernel methods augmented with random features give scalable algorithms for learning from big data. But it has been computationally hard to sample random features according to a probability distribution that is optimized for the data, so as to minimize the required number of features for achieving the learning to a desired accuracy. Here, we develop a quantum algorithm for sampling from this optimized distribution over features, in runtime $O(D)$ that is linear in the dimension $D$ of the input data. Our algorithm achieves an exponential speedup in $D$ compared to any known classical algorithm for this sampling task. In contrast to existing quantum machine learning algorithms, our algorithm circumvents sparsity and low-rank assumptions and thus has wide applicability. We also show that the sampled features can be combined with regression by stochastic gradient descent to achieve the learning without canceling out our exponential speedup. Our algorithm based on sampling optimized random features leads to an accelerated framework for machine learning that takes advantage of quantum computers.

The coronavirus (COVID-19) outbreak is having a growing impact on the global economy. So, how is the impact of COVID-19 going to be on the tech job market and what are the latest trends for data science, AI/ML, analytics, IoT, cloud computing? What are the key in-demand tech job profiles and domains during and after the COVID-19 phase? There have been more than 12,750 confirmed cases of COVID-19 in India so far. Between April 6 – 12, 46% and 39% of new confirmed cases have been reported in Europe and the USA respectively.