Reviewerly/exHarmony huggingface dataset:
A Parameter-Free Cleaning Method for SMOTE in Imbalanced Classification

Oversampling is an efficient technique in dealing with class-imbalance problem. It addresses the problem by reduplicating or generating the minority class samples to balance the distribution between the samples of the majority and the minority class. Synthetic minority oversampling technique (SMOTE) is one of the typical representatives. During the past decade, researchers have proposed many variants of SMOTE. However, the existing oversampling methods may generate wrong minority class samples in some scenarios. Furthermore, how to effectively mine the inherent complex characteristics of imbalanced data remains a challenge. To this end, this paper proposes a parameter-free data cleaning method to improve SMOTE based on constructive covering algorithm. The dataset generated by SMOTE is first partitioned into a group of covers, then the hard-to-learn samples can be detected based on the characteristics of sample space distribution. Finally, a pair-wise deletion strategy is proposed to remove the hard-to-learn samples. The experimental results on 25 imbalanced datasets show that our proposed method is superior to the comparison methods in terms of various metrics, such as F-measure, G-mean, and Recall. Our method not only can reduce the complexity of the dataset but also can improve the performance of the classification model.
Generative Adversarial Networks: A Survey and Taxonomy.

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are: (1) the generation of high quality images, (2) diversity of image generation, and (3) stable training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state of the art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Code related to GAN-variants studied in this work is summarized on this https URL.
Radical aggregation network for few-shot offline handwritten Chinese character recognition

Offline handwritten Chinese character recognition has attracted much interest due to its various applications. The most cutting-edge methods treat Chinese character as a whole, ignoring the structures and radicals that compose characters. To use the radical-level composition of Chinese characters and achieve few-shot/zero-shot Chinese character recognition, some methods attempt to recognize Chinese characters at the radical level; however, these methods have shown poor performance due to weak radical feature representation and the use of inflexible decoding algorithm. In this paper, a novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition. The RAN consists of three components, a radical mapping encoder (RME), a radical aggregation module (RAM), and a character analysis decoder (CAD). Experiments show that our method can effectively recognize unseen handwritten characters given few support samples, while maintaining a high performance on seen characters.
Unrestricted Adversarial Examples via Semantic Manipulation

Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against \emph{adversarial examples} which are carefully crafted samples with a small magnitude of the perturbation. Such adversarial perturbations are usually restricted by bounding their $\mathcal{L}_p$ norm such that they are imperceptible, and thus many current defenses can exploit this property to reduce their adversarial impact. In this paper, we instead introduce unrestricted perturbations that manipulate semantically meaningful image-based visual descriptors -- color and texture -- in order to generate effective and photorealistic adversarial examples. We show that these semantically aware perturbations are effective against JPEG compression, feature squeezing and adversarially trained model. We also show that the proposed methods can effectively be applied to both image classification and image captioning tasks on complex datasets such as ImageNet and MSCOCO. In addition, we conduct comprehensive user studies to show that our generated semantic adversarial examples are photorealistic to humans despite large magnitude perturbations when compared to other attacks.
Multilingual stance detection in social media political debates

Stance Detection is the task of automatically determining whether the author of a text is in favor, against, or neutral towards a given target. In this paper we investigate the portability of tools performing this task across different languages, by analyzing the results achieved by a Stance Detection system (i.e. MultiTACOS) trained and tested in a multilingual setting. First of all, a set of resources on topics related to politics for English, French, Italian, Spanish and Catalan is provided which includes: novel corpora collected for the purpose of this study, and benchmark corpora exploited in Stance Detection tasks and evaluation exercises known in literature. We focus in particular on the novel corpora by describing their development and by comparing them with the benchmarks. Second, MultiTACOS is applied with different sets of features especially designed for Stance Detection, with a specific focus to exploring and combining both features based on the textual content of the tweet (e.g., style and affective load) and features based on contextual information that do not emerge directly from the text. Finally, for better highlighting the contribution of the features that most positively affect system performance in the multilingual setting, a features analysis is provided, together with a qualitative analysis of the misclassified tweets for each of the observed languages, devoted to reflect on the open challenges.
Multidimensional Evaluation of Virtual Reality Paradigms in Clinical Neuropsychology: Application of the VR-Check Framework

Virtual reality (VR) represents a key technology of the 21st century, attracting substantial interest from a wide range of scientific disciplines. With regard to clinical neuropsychology, a multitude of new VR applications are being developed to overcome the limitations of classical paradigms. Consequently, researchers increasingly face the challenge of systematically evaluating the characteristics and quality of VR applications to design the optimal paradigm for their specific research question and study population. However, the multifaceted character of contemporary VR is not adequately captured by the traditional quality criteria (ie, objectivity, reliability, validity), highlighting the need for an extended paradigm evaluation framework. To address this gap, we propose a multidimensional evaluation framework for VR applications in clinical neuropsychology, summarized as an easy-to-use checklist (VR-Check). This framework rests on 10 main evaluation dimensions encompassing cognitive domain specificity, ecological relevance, technical feasibility, user feasibility, user motivation, task adaptability, performance quantification, immersive capacities, training feasibility, and predictable pitfalls. We show how VR-Check enables systematic and comparative paradigm optimization by illustrating its application in an exemplary research project on the assessment of spatial cognition and executive functions with immersive VR. This application furthermore demonstrates how the framework allows researchers to identify across-domain trade-offs, makes deliberate design decisions explicit, and optimizes the allocation of study resources. Complementing recent approaches to standardize clinical VR studies, the VR-Check framework enables systematic and project-specific paradigm optimization for behavioral and cognitive research in neuropsychology.
Abnormal Crowd Behavior Detection Using Motion Information Images and Convolutional Neural Networks

We introduce a novel method for abnormal crowd event detection in surveillance videos. Particularly, our work focuses on panic and escape behavior detection that may appear because of violent events and natural disasters. First, optical flow vectors are computed to generate a motion information image (MII) for each frame, and then MIIs are used to train a convolutional neural network (CNN) for abnormal crowd event detection. The proposed MII is a new formulation that provides a visual appearance of crowd motion. The proposed MIIs make the discrimination between normal and abnormal behaviors easier. The MII is mainly based on the optical flow magnitude, and angle difference computed between the optical flow vectors in consecutive frames. A CNN is employed to learn normal and abnormal crowd behaviors using MIIs. The MII generation, and the combination with a CNN is a new approach in the context of abnormal crowd behavior detection. Experiments are performed on commonly used datasets such as UMN and PETS2009. Evaluation indicates that our method achieves the best results.
Path Planning for ground robots in agriculture: a short review

The world's population is estimated to reach nine billion people by the year 2050, which indicates that agricultural productivity must increase sustainably. The mechanisation and automatisation of agricultural tasks is an essential step to face population growth. Ground robots have been developed along the last decade for several agricultural applications, being, the autonomous and safe navigation one of the hardest challenge in this development. Moving autonomously, a mobile platform involves different tasks, such as localisation, mapping, motion control, and path planning, a crucial step for autonomous operations. This article performs a survey of different applications for path planning techniques applied to various agricultural contexts. This paper analyses different agricultural applications and details about the employed path planning method. The conclusion indicates that path planning has been successfully applied to agrarian robots for field coverage and point-to-point navigation, being that coverage path planning is slightly more advanced in this field.
The Emerging Landscape of Explainable Automated Planning &amp; Decision Making

In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has emerged as a focus area in the last couple of years and contrast that with earlier efforts in the field in terms of techniques, target users, and delivery mechanisms. We hope that the survey will provide guidance to new researchers in automated planning towards the role of explanations in the effective design of human-in-the-loop systems, as well as provide the established researcher with some perspective on the evolution of the exciting world of explainable planning.
Plaintext attack on joint transform correlation encryption system by convolutional neural network

The image encryption system based on joint transform correlation has attracted much attention because its ciphertext does not contain complex value and can avoid strict pixel alignment of ciphertext when decryption occurs. This paper proves that the joint transform correlation architecture is vulnerable to the attack of the deep learning method-convolutional neural network. By giving the convolutional neural network a large amount of ciphertext and its corresponding plaintext, it can simulate the key of the encryption system. Unlike the traditional method which uses the phase recovery algorithm to retrieve or estimate optical encryption key, the key model trained in this paper can directly convert the ciphertext to the corresponding plaintext. Compared with the existing neural network systems, this paper uses the sigmoid activation function and adds dropout layers to make the calculation of the neural network more rapid and accurate, and the equivalent key trained by the neural network has certain robustness. Computer simulations prove the feasibility and effectiveness of this method.
MLO: Multi Leader Optimizer

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.
Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse

Quantum computers are traditionally operated by programmers at the granularity of a gate-based instruction set. However, the actual device-level control of a quantum computer is performed via analog pulses. We introduce a compiler that exploits direct control at this microarchitectural level to achieve significant improvements for quantum programs. Unlike quantum optimal control, our approach is bootstrapped from existing gate calibrations and the resulting pulses are simple. Our techniques are applicable to any quantum computer and realizable on current devices. We validate our techniques with millions of experimental shots on IBM quantum computers, controlled via the OpenPulse control interface. For representative benchmarks, our pulse control techniques achieve both 1.6x lower error rates and 2x faster execution time, relative to standard gate-based compilation. These improvements are critical in the near-term era of quantum computing, which is bottlenecked by error rates and qubit lifetimes.
Reinforcement learning-based application Autoscaling in the Cloud: A survey

Reinforcement Learning (RL) has demonstrated a great potential for automatically solving decision-making problems in complex, uncertain environments. RL proposes a computational approach that allows learning through interaction in an environment with stochastic behavior, where agents take actions to maximize some cumulative short-term and long-term rewards. Some of the most impressive results have been shown in Game Theory where agents exhibited superhuman performance in games like Go or Starcraft 2, which led to its gradual adoption in many other domains, including Cloud Computing. Therefore, RL appears as a promising approach for Autoscaling in Cloud since it is possible to learn transparent (with no human intervention), dynamic (no static plans), and adaptable (constantly updated) resource management policies to execute applications. These are three important distinctive aspects to consider in comparison with other widely used autoscaling policies that are defined in an ad-hoc way or statically computed as in solutions based on meta-heuristics. Autoscaling exploits the Cloud elasticity to optimize the execution of applications according to given optimization criteria, which demands deciding when and how to scale up/down computational resources and how to assign them to the upcoming processing workload. Such actions have to be taken considering that the Cloud is a dynamic and uncertain environment. Motivated by this, many works apply RL to the autoscaling problem in the Cloud. In this work, we exhaustively survey those proposals from major venues, and uniformly compare them based on a set of proposed taxonomies. We also discuss open problems and prospective research in the area.
Machine learning in medicine: a practical introduction to natural language processing

Abstract Background Unstructured text, including medical records, patient feedback, and social media comments, can be a rich source of data for clinical research. Natural language processing (NLP) describes a set of techniques used to convert passages of written text into interpretable datasets that can be analysed by statistical and machine learning (ML) models. The purpose of this paper is to provide a practical introduction to contemporary techniques for the analysis of text-data, using freely-available software. Methods We performed three NLP experiments using publicly-available data obtained from medicine review websites. First, we conducted lexicon-based sentiment analysis on open-text patient reviews of four drugs: Levothyroxine, Viagra, Oseltamivir and Apixaban. Next, we used unsupervised ML (latent Dirichlet allocation, LDA) to identify similar drugs in the dataset, based solely on their reviews. Finally, we developed three supervised ML algorithms to predict whether a drug review was associated with a positive or negative rating. These algorithms were: a regularised logistic regression, a support vector machine (SVM), and an artificial neural network (ANN). We compared the performance of these algorithms in terms of classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results Levothyroxine and Viagra were reviewed with a higher proportion of positive sentiments than Oseltamivir and Apixaban. One of the three LDA clusters clearly represented drugs used to treat mental health problems. A common theme suggested by this cluster was drugs taking weeks or months to work. Another cluster clearly represented drugs used as contraceptives. Supervised machine learning algorithms predicted positive or negative drug ratings with classification accuracies ranging from 0.664, 95% CI [0.608, 0.716] for the regularised regression to 0.720, 95% CI [0.664,0.776] for the SVM. Conclusions In this paper, we present a conceptual overview of common techniques used to analyse large volumes of text, and provide reproducible code that can be readily applied to other research studies using open-source software.
An Improved Light-Weight Traffic Sign Recognition Algorithm Based on YOLOv4-Tiny

Aiming at the problems of low detection accuracy and inaccurate positioning accuracy of light-weight network in traffic sign recognition task, an improved light-weight traffic sign recognition algorithm based on YOLOv4-Tiny was proposed. By improving the K-means clustering algorithm, the anchor with appropriate size is generated for the traffic sign data set to improve the detection recall rate and target positioning accuracy. The strategy of large-scale feature map optimization is proposed, which enriches the feature level of the network by using the low-level information, strengthens the representation of the feature information of the small target, and improves the detection accuracy of the long-range small target. In view of the problem of missed detection of high overlapping targets in the post-processing stage of the model, the paper proposes an improved NMS algorithm to screen the prediction box, avoid deleting the prediction results of different targets, and further improve the detection accuracy and recall rate of the target. Experimental results show that, compared with the original YOLOv4-Tiny algorithm, the improved algorithm in traffic sign recognition task based on TT100K dataset, mAP and recall are improved by 5.73% and 7.29% respectively, and FPS value is maintained at about 87 f/s, which meets the accuracy and real-time requirements of traffic sign recognition task.
An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning

Underwater video images, as the primary carriers of underwater information, play a vital role in human exploration and development of the ocean. Due to the optical characteristics of water bodies, underwater video images generally have problems such as color bias and unclear image quality, and image quality degradation is severe. Degenerated images have adverse effects on the visual tasks of underwater vehicles, such as recognition and detection. Therefore, it is vital to obtain high-quality underwater video images. Firstly, this paper analyzes the imaging principle of underwater images and the reasons for their decline in quality and briefly classifies various existing methods. Secondly, it focuses on the current popular deep learning technology in underwater image enhancement, and the underwater video enhancement technologies are also mentioned. It also introduces some standard underwater data sets, common video image evaluation indexes and underwater image specific indexes. Finally, this paper discusses possible future developments in this area.
2D hyperchaotic system based on Schaffer function for image encryption

Chaotic systems are the most essential tools for wide range of applications such as communication, watermarking, data compression and multimedia encryption. However, the existing chaotic systems suffer from low complexity and randomness. In this study, a novel 2D hyperchaotic system denoted as Schaffer map is conceived for high complexity required applications. It is inspired by the Schaffer function mostly used as optimization benchmark function to exploit its strict oscillation properties. The chaotic performance of 2D Schaffer map is evaluated over rigorous chaos indicators such as bifurcation and phase space trajectory diagrams, Lyapunov exponent (LE), sample entropy (SE), permutation entropy (PE), 0-1 test, correlation dimension (CD) and Kolmogorov entropy (KE). The proposed chaotic system is verified through comparison with its recent counterparts. The 2D Schaffer map manifests the best ergodicity and erraticity characteristics. In addition, the applicability of the 2D Schaffer map is tested by implementing it to image encryption. The results show that the proposed 2D Schaffer map has excellent hyperchaotic performance thanks to its diversity property.