Reviewerly/exHarmony huggingface dataset:
Near-Optimal Area-Coverage Path Planning of Energy-Constrained Aerial Robots With Application in Autonomous Environmental Monitoring

This article describes a Voronoi-based path generation (VPG) algorithm for an energy-constrained mobile robot, such as an unmanned aerial vehicle (UAV). The algorithm solves a variation of the coverage path-planning problem where complete coverage of an area is not possible due to path-length limits caused by energy constraints on the robot. The algorithm works by modeling the path as a connected network of mass-spring-damper systems. The approach further leverages the properties of Voronoi diagrams to generate a potential field to move path waypoints to near-optimal configurations while maintaining path-length constraints. Simulation and physical experiments on an aerial vehicle are described. Simulated runtimes show linear-time complexity with respect to the number of path waypoints. Tests in variously shaped areas demonstrate that the method can generate paths in both convex and nonconvex areas. Comparison tests with other path generation methods demonstrate that the VPG algorithm strikes a good balance between runtime and optimality, with significantly better runtime than direct optimization, lower cost coverage paths than a lawnmower-style coverage path, and moderately better performance in both metrics than the most conceptually similar method. Physical experiments demonstrate the applicability of the VPG method to a physical UAV, and comparisons between real-world results and simulations show that the costs of the generated paths are within a few percent of each other, implying that analysis performed in simulation will hold for real-world application, assuming that the robot is capable of closely following the path and a good energy model is available. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —For autonomous mobile-robotics-based applications where a robot equipped with a tool or sensor is required to survey an area for inspection, monitoring, cleaning, and so on, effectively covering the area is desirable. However, for energy-constrained systems such as aerial vehicles with limited flight time, complete coverage is not possible. Presented here is a new Voronoi-based path generation algorithm that takes energy constraints into account to generate waypoints for the robot to follow in a near-optimal configuration while maintaining path-length constraints. The approach is applied in simulation and experiments for an application in environmental monitoring using unmanned aerial vehicles.
Progressive perception-oriented network for single image super-resolution

Recently, it has been demonstrated that deep neural networks can significantly improve the performance of single image super-resolution (SISR). Numerous studies have concentrated on raising the quantitative quality of super-resolved (SR) images. However, these methods that target PSNR maximization usually produce blurred images at large upscaling factor. The introduction of generative adversarial networks (GANs) can mitigate this issue and show impressive results with synthetic high-frequency textures. Nevertheless, these GAN-based approaches always have a tendency to add fake textures and even artifacts to make the SR image of visually higher-resolution. In this paper, we propose a novel perceptual image super-resolution method that progressively generates visually high-quality results by constructing a stage-wise network. Specifically, the first phase concentrates on minimizing pixel-wise error, and the second stage utilizes the features extracted by the previous stage to pursue results with better structural retention. The final stage employs fine structure features distilled by the second phase to produce more realistic results. In this way, we can maintain the pixel, and structural level information in the perceptual image as much as possible. It is useful to note that the proposed method can build three types of images in a feed-forward process. Also, we explore a new generator that adopts multi-scale hierarchical features fusion. Extensive experiments on benchmark datasets show that our approach is superior to the state-of-the-art methods. Code is available at https://github.com/Zheng222/PPON.
Duplicate Bug Report Detection Using Dual-Channel Convolutional Neural Networks

Developers rely on bug reports to fix bugs. The bug reports are usually stored and managed in bug tracking systems. Due to the different expression habits, different reporters may use different expressions to describe the same bug in the bug tracking system. As a result, the bug tracking system often contains many duplicate bug reports. Automatically detecting these duplicate bug reports would save a large amount of effort for bug analysis. Prior studies have found that deep-learning technique is effective for duplicate bug report detection. Inspired by recent Natural Language Processing (NLP) research, in this paper, we propose a duplicate bug report detection approach based on Dual-Channel Convolutional Neural Networks (DC-CNN). We present a novel bug report pair representation, i.e., dual-channel matrix through concatenating two single-channel matrices representing bug reports. Such bug report pairs are fed to a CNN model to capture the correlated semantic relationships between bug reports. Then, our approach uses the association features to classify whether a pair of bug reports are duplicate or not. We evaluate our approach on three large datasets from three open-source projects, including Open Office, Eclipse, Net Beans and a larger combined dataset, and the accuracy of classification reaches 0.9429, 0.9685, 0.9534, 0.9552 respectively. Such performance outperforms the two state-of-the-art approaches which also use deep-learning techniques. The results indicate that our dual-channel matrix representation is effective for duplicate bug report detection.
Autonomous Quadcopter Precision Landing Onto a Heaving Platform: New Method and Experiment

Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method.
Effective Visual Domain Adaptation via Generative Adversarial Distribution Matching

In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. Generative adversarial network (GAN) loss is widely used in adversarial adaptation learning methods to reduce an across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, we put forward a novel adaptation framework called generative adversarial distribution matching (GADM). In GADM, we improve the objective function by taking cross-domain discrepancy distance into consideration and further minimize the difference through the competition between a generator and discriminator, thereby greatly decreasing cross-domain distribution difference. Experimental results and comparison with several state-of-the-art methods verify GADM's superiority in image classification across domains.
Machine Learning in Oncology: What Should Clinicians Know?

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.
Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images

In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis.
A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email

Identification of emotion hidden in limited text is an active research problem. This work presents a framework for the same using email text. The present work is based on machine learning methods and utilizes three classifiers and three feature selection methods. The novelty of the proposed framework is the utilization of in-text features to identify emotion contained in short texts and development of a dataset for this purpose. Six emotions, namely, neutral, happy, sad, angry, positively surprised, and negatively surprised are utilized here based on baseline theories on human emotion. Experiments are performed on three datasets including a benchmark and one local dataset. These experiments are performed by extracting 14 in-text features from the data. The proposed framework is evaluated using four standard evaluation metrics. Based on the feature selection results, experiments are performed on the datasets under consideration by vertically partitioning them into all features, top features, and bottom features. Qualitative and quantitative comparison of the proposed work is also made with two state-of-the-art methods. The obtained results suggest better performance of the current work with an average accuracy of 83%. The proposed framework can be utilized in an assortment of domains to identify human emotion by providing limited text as an input.
Neural machine translation: Challenges, progress and future

Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT. This article makes a review of NMT framework, discusses the challenges in NMT, introduces some exciting recent progresses and finally looks forward to some potential future research trends.
Artificial Intelligence-based Voice Assistant

Voice control is a major growing feature that change the way people can live. The voice assistant is commonly being used in smartphones and laptops. AI-based Voice assistants are the operating systems that can recognize human voice and respond via integrated voices. This voice assistant will gather the audio from the microphone and then convert that into text, later it is sent through GTTS (Google text to speech). GTTS engine will convert text into audio file in English language, then that audio is played using play sound package of python programming Language.
Image encryption algorithm based on Tent-Dynamics coupled map lattices and diffusion of Household

This paper presents an image encryption algorithm based on Tent-Dynamics Coupled Map Lattices (TDCML) system and diffusion of Household. All aspects of the TDCML spatiotemporal chaotic system proposed in this paper meet the cryptographic characteristics and are suitable for studying image encryption. The specific image encryption algorithm first generates chaotic sequences according to the TDCML system. The image scrambling uses a cyclic shift algorithm, and the movement of each row and column is determined by the chaotic sequence. Then the image diffusion is improved according to the Household orthogonal decomposition method. Think of each column of the image matrix as a row vector, and then operate according to part of the formulas in the Household transformation to obtain a new row vector. Finally, perform the bitwise XOR operation with the specified chaotic sequence to obtain the final encrypted image. Theoretical analysis and experimental results have proved the security of this encryption algorithm.
CDBH: A clustering and density-based hybrid approach for imbalanced data classification

The problem of imbalanced data set classification is prevalent in the studies of machine learning and data mining. In these kinds of data sets, the number of samples in classes is unequal so that one class has a lot more samples (the majority or negative class) than the other (the minority or positive class). The classical classifiers are ineffective in these conditions because they are biased toward the majority class and ignore the minority class, which is more important. Preprocessing the data distribution before training the classifier is one of the most effective methods to resolve this problem. These methods, balance the data distribution by decreasing the majority class size (under-sampling methods) or increasing the minority class size (over-sampling methods) or combining both of them (hybrid methods). In this paper, we propose an effective and simple hybrid approach based on the density concept and clustering, which is called Clustering and Density-Based Hybrid (CDBH). First, the minority class samples are clustered by the well-known k-means algorithm and their densities in each cluster are obtained. Then, the denser minority samples are selected with more likely to generate the new minority samples. To decrease the majority class size, the k-means algorithm is applied again on the majority class samples to cluster them and compute their densities, like the previous stage. Finally, the denser majority samples will have more chance to choose from the training set, and other samples are removed to balance the data samples distribution between classes. In the experiments, the Support Vector Machine (SVM) classifier is used as the classifier, and F-measure and AUC criteria are employed for evaluation. Also, preprocessing methods are compared in terms of the complexity of the classification model and the over-sampling rate. The results of comparing CDBH and other state of the art methods over 44 imbalanced data sets show the superiority of the proposed CDBH method based on the F-measure criterion.
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities

We present a method that "meta" classifies whether segments predicted by a semantic segmentation neural network intersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input scene's size. We aggregate these dispersion measures segment-wise and derive metrics that are well correlated with the segment-wise IoU of prediction and ground truth. This procedure yields an almost plug and play post-processing tool to rate the prediction quality of semantic segmentation networks on segment level. This is especially relevant for monitoring neural networks in online applications like automated driving or medical imaging where reliability is of utmost importance. In our tests, we use publicly available state-of-the-art networks trained on the Cityscapes dataset and the BraTS2017 dataset and analyze the predictive power of different metrics as well as different sets of metrics. To this end, we compute logistic LASSO regression fits for the task of classifying IoU = 0 vs. IoU > 0 per segment and obtain AUROC values of up to 91.55%. We complement these tests with linear regression fits to predict the segment-wise IoU and obtain prediction standard deviations of down to 0.130 as well as R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values of up to 84.15%. We show that these results clearly outperform standard approaches.
Ground truthing from multi-rater labeling with three-way decision and possibility theory

In recent years, Machine Learning (ML) has attracted wide interest as aid for decision makers in complex domains, such as medicine. Although domain experts are typically aware of the intrinsic uncertainty around it, the issue of Ground Truth (GT) quality has scarcely been addressed in the ML literature. GT quality is regularly assumed to be adequate, regardless of the number and skills of raters involved in data annotation. These factors can, however, potentially have a severe negative impact on the reliability of ML models. In this article we study the influence of GT quality, in terms of number of raters, their expertise, and their agreement level, on the performance of ML models. We introduce the concept of reduction: computational procedures by which to produce single-target GT from multi-rater settings. We propose three reductions, based on three-way decision, possibility theory, and probability theory. We provide characterizations of these reductions from the perspective of learning theory and propose two ML algorithms. We report the result of experiments, on both real-world medical and synthetic datasets, showing that GT quality strongly impacts on the performance of ML models, and that the proposed algorithms can better handle this form of uncertainty compared with state-of-the-art approaches.
Exploiting Unintended Property Leakage in Blockchain-Assisted Federated Learning for Intelligent Edge Computing

Federated learning (FL) serves as an enabling technology for intelligent edge computing, where high-quality machine learning (ML) models are collaboratively trained over large amounts of data generated by various Internet of Things devices while preserving data privacy. To further provide data confidentiality, computation auditability, and participant incentives, the blockchain framework has been incorporated into FL. However, it is an open question whether the model updates from participants in blockchain-assisted FL can disclose properties of the private data the participants are unintended to share. In this article, we propose a novel property inference attack that exploits the unintended property leakage in blockchain-assisted FL for intelligent edge computing. More specifically, we present an active attack to learn the property leakage from model updates of participants and to identify a set of participants with a certain property. We also design a dynamic participant selection strategy tailored to the setting of large-scale FL, which accelerates the selection process of target participants and improves attack accuracy. We evaluate the proposed attack through extensive experiments with publicly available data sets. The experimental results demonstrate that the proposed attack is effective and efficient in inferring various properties of training data, while maintaining the high quality of the main tasks in FL.
Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data

Feature selection is an important preprocessing and interpretable method in the fields where big data plays an essential role. In this paper, we first reformulate and analyze some representative information theoretic feature selection methods from the perspective of approximations of feature inner correlations, and indicate that many of these methods cannot guarantee any theoretical bounds of feature inner correlations. We thus introduce two lower bounds that have very simple forms for feature redundancy and complementarity, and verify that they are closer to the optima than the existing lower bounds applied by some state-of-the-art information theoretic methods. A simple and effective feature selection method based on the proposed lower bounds is then proposed and empirically verified with a wide scope of real-world datasets. The experimental results show that the proposed method achieves promising improvement on feature selection, indicating the effectiveness of the feature criterion consisting of the proposed lower bounds of redundancy and complementarity.