Reviewerly/exHarmony huggingface dataset:
Adversarial active learning for the identification of medical concepts and annotation inconsistency

Named entity recognition (NER) is a principal task in the biomedical field and deep learning-based algorithms have been widely applied to biomedical NER. However, all of these methods that are applied to biomedical corpora use only annotated samples to maximize their performances. Thus, (1) large numbers of unannotated samples are relinquished and their values are overlooked. (2) Compared with other types of active learning (AL) algorithms, generative adversarial learning (GAN)-based AL methods have developed slowly. Furthermore, current diversity-based AL methods only compute similarities between a pair of sentences and cannot evaluate distribution similarities between groups of sentences. Annotation inconsistency is one of the significant challenges in the biomedical annotation field. Most existing methods for addressing this challenge are statistics-based or rule-based methods. (3) They require sufficient expert knowledge and complex designs. To address challenges (1), (2), and (3) simultaneously, we propose innovative algorithms.GAN is introduced in this paper, and we propose the GAN-bidirectional long short-term memory-conditional random field (GAN-BiLSTM-CRF) and the GAN-bidirectional encoder representations from transformers-conditional random field (GAN-BERT-CRF) models, which can be considered an NER model, an AL model, and a model identifying error labels. BiLSTM-CRF or BERT-CRF is defined as the generator and a convolutional neural network (CNN)-based network is considered the discriminator. (1) The generator employs unannotated samples in addition to annotated samples to maximize NER performance. (2) The outputs of the CRF layer and the discriminator are used to select unlabeled samples for the AL task. (3) The discriminator discriminates the distribution of error labels from that of correct labels, identify error labels, and address the annotation inconsistency challenge.The corpus from the 2010 i2b2/VA NLP challenge and the Chinese CCKS-2017 Task 2 dataset are adopted for experiments. Compared to the baseline BiLSTM-CRF and BERT-CRF, the GAN-BiLSTM-CRF and GAN-BERT-CRF models achieved significant improvements on the precision, recall, and F1 scores in terms of NER performance. Learning curves in AL experiments show the comparative results of the proposed models. Furthermore, the trained discriminator can identify samples with incorrect medical labels in both simulation and real-word experimental environments.The idea of introducing GAN contributes significant results in terms of NER, active learning, and the ability to identify incorrect annotated samples. The benefits of GAN will be further studied.
A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies

For many decades, Machine Learning made it possible for humans to map the patterns that govern interpolating problems and also, provided methods to cluster and classify big amount of uncharted data. In recent years, optimization problems which can be mathematically formulated and are hard to be solved with simple or naïve heuristic methods brought up the need for new methods, namely Evolutionary Strategies. These methods are inspired by strategies that are met in flora and fauna in nature. However, a lot of these methods are called nature-inspired when there is no such inspiration in their algorithmic model. Furthermore, even more evolutionary schemes are presented each year, but the lack of applications makes them of no actual utility. In this chapter, all Swarm Intelligence methods as far as the methods that are not inspired by swarms, flocks or groups, but still derive their inspiration by animal behaviors are collected. The applications of these two sub-categories are investigated and some preliminary findings are presented to highlight some main points for Nature Inspired Intelligence utility.
Classifying Imbalanced Multi-modal Sensor Data for Human Activity Recognition in a Smart Home using Deep Learning

In smart homes, data generated from real-time sensors for human activity recognition is complex, noisy and imbalanced. It is a significant challenge to create machine learning models that can classify activities which are not as commonly occurring as other activities. Machine learning models designed to classify imbalanced data are biased towards learning the more commonly occurring classes. Such learning bias occurs naturally, since the models better learn classes which contain more records. This paper examines whether fusing real-world imbalanced multi-modal sensor data improves classification results as opposed to using unimodal data; and compares deep learning approaches to dealing with imbalanced multi-modal sensor data when using various resampling methods and deep learning models. Experiments were carried out using a large multi-modal sensor dataset generated from the Sensor Platform for HEalthcare in a Residential Environment (SPHERE). The data comprises 16104 samples, where each sample comprises 5608 features and belongs to one of 20 activities (classes). Experimental results using SPHERE demonstrate the challenges of dealing with imbalanced multi-modal data and highlight the importance of having a suitable number of samples within each class for sufficiently training and testing deep learning models. Furthermore, the results revealed that when fusing the data and using the Synthetic Minority Oversampling Technique (SMOTE) to correct class imbalance, CNN-LSTM achieved the highest classification accuracy of 93.67% followed by CNN, 93.55%, and LSTM, i.e. 92.98%.
A method of feasible trajectory planning for UAV formation based on bi-directional fast search tree

This paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formation to improve the adaptability of UAV formation to complex dynamic environment. Firstly, the obstacle and formation constraint model in complex environment are established, and a Bi-directional-Rapidly Exploring Random Tree (Bi-RRT*) algorithm based on greedy strategy is proposed to realize obstacle avoidance, sudden response and formation reconstruction for UAV formation; it improves the efficiency and formation stability of the current optimization algorithm in complex environments. Then, this paper ameliorates the extension of algorithm nodes to improve the efficiency of the solution, the optimization method of nodes growth greatly not only avoid the invalid extension, but also save storage space and improve the convergence speed of the algorithm. In addition, this paper adopts the adaptive step size rolling detection method to improve the sensitivity of the UAV formation to the sudden threat. The experimental results show that the complex environment constraint model proposed in this paper combined with the improved Bi-RRT* algorithm can effectively improve the adaptive ability of UAV formation to the dynamic environment, and it also can plan the ideal trajectory under sudden threats, which greatly improving the survivability of the UAV formation.
<i>treeheatr</i>: an R package for interpretable decision tree visualizations

Abstract Summary treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. Availability and implementation The treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration.
Recent Advances in Conversational Information Retrieval

Recent progress in deep learning has brought tremendous improvements in conversational AI, leading to a plethora of commercial conversational services that allow naturally spoken interactions, increasing the need for more human-centric interactions in IR. As a result, we have witnessed a resurgent interest in developing modern CIR systems in research communities and industry. This tutorial presents recent advances in CIR, focusing mainly on neural approaches and new applications developed in the past five years. Our goal is to provide a thorough and in-depth overview of the general definition of CIR, the components of CIR systems, new applications raised for its conversational aspects, and the (neural) techniques recently developed for it.
Development of Smartphone-Based Human-Robot Interfaces for Individuals With Disabilities

Persons with disabilities often rely on caregivers or family members to assist in their daily living activities. Robotic assistants can provide an alternative solution if intuitive user interfaces are designed for simple operations. Current human-robot interfaces are still far from being able to operate in an intuitive way when used for complex activities of daily living (ADL). In this era of smartphones that are packed with sensors, such as accelerometers, gyroscopes and a precise touch screen, robot controls can be interfaced with smartphones to capture the user's intended operation of the robot assistant. In this letter, we review the current popular human-robot interfaces, and we present three novel human-robot smartphone-based interfaces to operate a robotic arm for assisting persons with disabilities in their ADL tasks. Useful smartphone data, including 3 dimensional orientation and 2 dimensional touchscreen positions, are used as control variables to the robot motion in Cartesian teleoperation. In this letter, we present the three control interfaces, their implementation on a smartphone to control a robotic arm, and a comparison between the results on using the three interfaces for three different ADL tasks. The developed interfaces provide intuitiveness, low cost, and environmental adaptability.
Implementation Of underwater target tracking techniques for Gaussian and non-Gaussian environments

There are several techniques implemented, in an underwater target tracking environment, for the nonlinear dynamic systems in Gaussian and non-Gaussian environments. It is assumed with non-Gaussian distribution to make the problem part of the non-Gaussian distribution, and is measured in terms of calculations of plenty of scenarios simulated to validate the potential of the sub-optimal filter.This research is further carried out by considering two categories of non-Gaussian noises i.e.a mixture of Gaussian noises and shot noise. To evaluate tracking in Gaussian and non-Gaussian noises, the suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF algorithms are considered. Gaussian noise is a statistical noise having probability density function equal to the normal distribution function. The suboptimal filters, Extended Kalman filter, and Unscented Kalman filter (UKF) algorithms are considered to evaluate tracking in Gaussian and non-Gaussian noises. To make further evaluation of the above said algorithms, they are compared with theoretical Cramer-Rao lower bound. The efficiency of UKF is in terms of percentage of non-Gaussian noise corrupted measurements, for which solution is obtained within a short time. The application of Monte-Carlo method at this simulations trapped accurate results.
Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement

Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a target image in another domain. However, three big challenges remain in image-to-image translation: (1) the lack of large amounts of aligned training pairs for various tasks; (2) the ambiguity of multiple possible outputs from a single input image; and (3) the lack of simultaneous training for multi-domain translation with a single network. Therefore in this paper, we propose a unified framework for learning to generate diverse outputs using unpaired training data and allow for simultaneous multi-domain translation via a single model. Moreover, we also observed from experiments that the implicit disentanglement of content and style could lead to undesirable results. Thus we investigate how to extract domain-level signal as explicit supervision so as to achieve better image-to-image translation. Extensive experiments show that the proposed method outperforms or is comparable with the state-of-the-art methods for various applications.
Assessment of solar power potential in a hill state of India using remote sensing and Geographic Information System

Estimation of the solar energy potential of the area is a pre-requisite for the large-scale deployment of photovoltaic (PV) panels. This study summarizes the solar PV potential at the rooftop areas of Uttarakhand through satellite imageries. The available rooftop area for solar PV system installation was estimated by a three-step hierarchical process including physical, geographic, and technical aspects. Our results showed that 58% area of the state receives solar radiation greater than 4 kWh/m2/day throughout the year. Using median efficiency (15%) of PV panels, the rooftop solar energy generation potential in hill region of Uttarakhand was estimated at 9.1 GWh from January to March, 12.7 GWh from April to June, 12.4 GWh from July to September and 7.7 GWh from October to December. If all available high irradiance roof-space (technical potential) is utilized for solar power generation it contributes about 57% of the state electricity consumption. The PV potential of field survey reports 27.47 GWh electricity output from the 234.4 MW ground-mounted solar PV systems and 5.9 MW rooftop solar PV systems in the plain district of Uttarakhand. Despite having high solar insolation, hills are ignored as potential solar energy sites due to their topography. Utilizing the flat rooftop surface to harvesting solar energy via solar PV panels can be a solution to issues arising due to undulating terrains of the mountains.
An Efficient Cold Start Solution for Recommender Systems Based on Machine Learning and User Interests

Recommender Systems are used to provide suggestions to users based on their interests. One of the well-known problems in recommender systems is the cold start problem, which concentrates on providing recommendations when the user data is not sufficient. Although many solutions have been proposed in the literature, the majority did not concentrate on using both the hidden user motifs. We proposed previously a user-interest based cold start solution, however, finding similar users needed optimization. In this work, we propose the use of machine learning techniques to find patterns associating user profile information and user extracted interests. This would further improve the accuracy provided suggestions for new users. Experimental work showed that our solution is efficient in terms of training time, classification time, and accuracy. In details, using Bayesian Classifier Chain classifier proved to be the fastest training and classification time, while all classifiers proved to be efficient in term of accuracy.
Modeling and analysis of block arrival times in the Bitcoin blockchain

In the original Bitcoin paper and other research papers since, it is assumed that Bitcoin blocks are mined at the instants of a homogeneous Poisson process. Based on blockchain block arrival data and stochastic analysis of the block arrival process, we demonstrate that this is not the case. We present a framework for studying the block arrival process, including two refined mathematical models for block arrivals, determined by combining models for a period of constant difficulty and how the difficulty evolves over time.
Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

Leukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.
Blockchain Enabled Online-Voting System

A blockchain-enabled online-voting system is being proposed in this following paper. Blockchain technologies deliver an endless variety of applications that benefit from distributed economies. The proposed model is an android application that has enhanced security features which includes both authentication and authorization. Authentication is incorporated by using a unique identification key and authorization is done by using fingerprint. Voters are also being verified by One-time password. The security in this project is implemented by using a 128 bit AES encryption algorithm and SHA-256 along with blockchain. The vote is casted in the form of transaction, where a blockchain is created, which keeps track of tallies of votes. Through this atomicity and integrity are maintained. [4] Index Terms—Voting System , Blockchain , SHA-256, Security, AES-128, Ballot
Privacy-Preserving Incentive Systems with Highly Efficient Point-Collection

Incentive systems (such as customer loyalty systems) are omnipresent nowadays and deployed in several areas such as retail, travel, and financial services. Despite the benefits for customers and companies, this involves large amounts of sensitive data being transferred and analyzed. These concerns initiated research on privacy-preserving incentive systems, where users register with a provider and are then able to privately earn and spend incentive points.
Bayesian Context Trees: Modelling and Exact Inference for Discrete Time Series

Abstract We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the context tree weighting alg-orithm can compute the prior predictive likelihood exa-ctly (averaged over both models and parameters), and two related algorithms are introduced, which identify the a posteriori most likely models and compute their exact posterior probabilities. All three algorithms are deterministic and have linear-time complexity. A family of variable-dimension Markov chain Monte Carlo samplers is also provided, facilitating further exploration of the posterior. The performance of the proposed methods in model selection, Markov order estimation and prediction is illustrated through simulation experiments and real-world applications with data from finance, genetics, neuroscience and animal communication. The associated algorithms are implemented in the R package BCT.
PDE-Based Dynamic Density Estimation for Large-Scale Agent Systems

Large-scale agent systems have foreseeable applications in the near future. Estimating their macroscopic density is critical for many density-based optimization and control tasks, such as sensor deployment and city traffic scheduling. In this letter, we study the problem of estimating their dynamically varying probability density, given the agents' individual dynamics (which can be nonlinear and time-varying) and their states observed in real-time. The density evolution is shown to satisfy a linear partial differential equation uniquely determined by the agents' dynamics. We present a density filter which takes advantage of the system dynamics to gradually improve its estimation and is scalable to the agents' population. Specifically, we use kernel density estimators (KDE) to construct a noisy measurement and show that, when the agents' population is large, the measurement noise is approximately “Gaussian”. With this important property, infinite-dimensional Kalman filters are used to design density filters. It turns out that the covariance of measurement noise depends on the true density. This state-dependence makes it necessary to approximate the covariance in the associated operator Riccati equation, rendering the density filter suboptimal. The notion of input-to-state stability is used to prove that the performance of the suboptimal density filter remains close to the optimal one. Simulation results suggest that the proposed density filter is able to quickly recognize the underlying modes of the unknown density and automatically ignore outliers, and is robust to different choices of kernel bandwidth of KDE.
Fast UAV Trajectory Optimization using Bilevel Optimization with Analytical Gradients

We present an efficient optimization framework that solves trajectory optimization problems by decoupling state variables from timing variables, thereby decomposing a challenging nonlinear programming (NLP) problem into two easier subproblems. With timing fixed, the state variables can be optimized efficiently using convex optimization, and the timing variables can be optimized in a separate NLP, which forms a bilevel optimization problem. The challenge is to obtain the gradient of the objective function which itself needs an optimization to compute. Whereas finite differences must solve many optimization problems to compute the gradient, our method is based on sensitivity analysis of parametric programming: the dual solution (Lagrange multipliers) of the lower-level optimization is used to compute analytical gradients. Since the dual solution is a by-product of the optimization, the exact gradients can be obtained "for free". The framework is demonstrated on generating trajectories in safe corridors for an unmanned aerial vehicle. Experiments demonstrate that bilevel optimization converges significantly more reliably than a standard NLP solver, and analytical gradients outperform finite differences in terms of computation speed and accuracy. With a 25ms cutoff time, our approach achieves over 8 times better suboptimality than the current state-of-the-art.