Reviewerly/exHarmony huggingface dataset:
High-Dimensional Unbalanced Binary Classification by Genetic Programming with Multi-Criterion Fitness Evaluation and Selection

High-dimensional unbalanced classification is challenging because of the joint effects of high dimensionality and class imbalance. Genetic programming (GP) has the potential benefits for use in high-dimensional classification due to its built-in capability to select informative features. However, once data are not evenly distributed, GP tends to develop biased classifiers which achieve a high accuracy on the majority class but a low accuracy on the minority class. Unfortunately, the minority class is often at least as important as the majority class. It is of importance to investigate how GP can be effectively utilized for high-dimensional unbalanced classification. In this article, to address the performance bias issue of GP, a new two-criterion fitness function is developed, which considers two criteria, that is, the approximation of area under the curve (AUC) and the classification clarity (i.e., how well a program can separate two classes). The obtained values on the two criteria are combined in pairs, instead of summing them together. Furthermore, this article designs a three-criterion tournament selection to effectively identify and select good programs to be used by genetic operators for generating offspring during the evolutionary learning process. The experimental results show that the proposed method achieves better classification performance than other compared methods.
Python power-up: new image tool visualizes complex data

The image viewing and analysis software napari has filled a gap in the programming language’s scientific ecosystem. The image viewing and analysis software napari has filled a gap in the programming language’s scientific ecosystem.
Medical named entity recognition based on dilated convolutional neural network

Named entity recognition (NER) is a fundamental and important task in natural language processing. Existing methods attempt to utilize convolutional neural network (CNN) to solve NER task. However, a disadvantage of CNN is that it fails to obtain the global information of texts, leading to an unsatisfied performance on medical NER task. In view of the disadvantages of CNN in medical NER task, this paper proposes to utilize the dilated convolutional neural network (DCNN) and bidirectional long short-term memory (BiLSTM) for hierarchical encoding, and make use of the advantages of DCNN to capture global information with fast computing speed. At the same time, multiple feature words are inserted into the medical text datasets for improving the performance of medical NER. Extensive experiments are done on three real-world datasets, which demonstrate that our method is superior to the compared models.
G6: A web-based library for graph visualization

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills. Although existing libraries and tools reduce the difficulty of generating graph visualization, there are still many challenges. We work closely with developers and formulate several design goals, then design and implement G6, a web-based library for graph visualization. It combines template-based configuration for high usability and flexible customization for high expressiveness. To enhance development efficiency, G6 proposes a range of optimizations, including state management and interaction modes. We demonstrate its capabilities through an extensive gallery, a quantitative performance evaluation, and an expert interview. G6 was first released in 2017 and has been iterated for 317 versions. It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.
Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform.
Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network

Current fully supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccurate facial shape constraints and insufficient labeled training samples. In this article, we propose a semisupervised framework, that is, a self-calibrated pose attention network (SCPAN) to achieve more robust and precise facial landmark detection in challenging scenarios. To be specific, a boundary-aware landmark intensity (BALI) field is proposed to model more effective facial shape constraints by fusing boundary and landmark intensity field information. Moreover, a self-calibrated pose attention (SCPA) model is designed to provide a self-learned objective function that enforces intermediate supervision without label information by introducing a self-calibrated mechanism and a pose attention mask. We show that by integrating the BALI fields and SCPA model into a novel SCPAN, more facial prior knowledge can be learned and the detection accuracy and robustness of our method for faces with large poses and heavy occlusions have been improved. The experimental results obtained for challenging benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in the literature.
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks

Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiable operators from fuzzy or real-valued logic that are parameter-free thus diminishing their capacity to fit the data, other approaches are only loosely based on logic making it difficult to interpret the learned ``rules". In this paper, we propose learning rules with the recently proposed logical neural networks (LNN). Compared to others, LNNs offer a strong connection to classical Boolean logic thus allowing for precise interpretation of learned rules while harboring parameters that can be trained with gradient-based optimization to effectively fit the data. We extend LNNs to induce rules in first-order logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable and can achieve comparable or higher accuracy due to their flexible parameterization.
Quantum Kernels for Real-World Predictions Based on Electronic Health Records

Research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretical work has shown provable advantage on synthetic data sets, no work done to date has studied empirically whether quantum advantage is attainable and with what data. In this paper, we report the first systematic investigation of empirical quantum advantage (EQA) in healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic health records (EHRs) data subsets and created a configuration space of 5-20 features and 200-300 training samples. For each configuration coordinate, we trained classical support vector machine (SVM) models based on radial basis function (RBF) kernels and quantum models with custom kernels using an IBM quantum computer, making this one of the largest quantum machine learning experiments to date. We empirically identified regimes where quantum kernels could provide advantage and introduced a terrain ruggedness index, a metric to help quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step towards a priori identification of data sets where quantum advantage could exist.
Convex Aggregation for Opinion Summarization

Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors.As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the aggregated latent vectors of inputs.More specifically, they perform the aggregation via simple average.However, little is known about how the vector aggregation step affects the generation quality.In this study, we revisit the commonly used simple average approach by examining the latent space and generated summaries.We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected L 2 -norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration.To overcome this issue, we develop a framework COOP, which searches input combinations for the latent vector aggregation using input-output word overlap.Experimental results show that COOP successfully alleviates the summary vector degeneration issue and establishes new state-of-theart performance on two opinion summarization benchmarks.Code is available at https: //github.com/megagonlabs/coop.
Polarization-Based Haze Removal Using Self-Supervised Network

Atmospheric scattering caused by suspended particles in the air severely degrades the scene radiance. This paper proposes a method to remove haze by using a neural network that combines scene polarization information. The neural network is self-supervised and online globally optimization can be achieved by using the atmospheric transmission model and gradient descent. Therefore, the proposed method does not require any haze-free image as the constraint for neural network training. The proposed approach is far superior to supervised algorithms in the performance of dehazing and is highly robust to the scene. It is proved that this method can significantly improve the contrast of the original image, and the detailed information of the scene can be effectively enhanced.
Deep Learning for Photonic Design and Analysis: Principles and Applications

Innovative techniques play important roles in photonic structure design and complex optical data analysis. As a branch of machine learning, deep learning can automatically reveal the inherent connections behind the data by using hierarchically structured layers, which has found broad applications in photonics. In this paper, we review the recent advances of deep learning for the photonic structure design and optical data analysis, which is based on the two major learning paradigms of supervised learning and unsupervised learning. In addition, the optical neural networks with high parallelism and low energy consuming are also highlighted as novel computing architectures. The challenges and perspectives of this flourishing research field are discussed.
A Comparative Study of 2D and 3D Digital Image Correlation Approaches for the Characterization and Numerical Analysis of Composite Materials

This article makes a comparison between different Digital Image Correlation methods to determinate the main mechanical characteristics of composite materials. More specifically Carbon Fiber Reinforced Polymers. For this purpose, several tensile tests were carried out using the same camera and lens model. Different statistical methods as well as probabilistic numerical simulations were performed with the aim of evaluating the discrepancies between methods, and between different mechanical parameters. We want to highlight the consistency of the results, enabling the possibility of using 3D methods with non-planar specimen for determining the mechanical properties of Carbon Fiber Reinforced Polymers. In this case, the novelty is focused on the use of different configurations (2D and 3D) to study the differences in terms of results. the objective is not the specific characterization of CFRP, but to analyze the way in which the use of a dataset from DIC3D or, on the contrary, from DIC2D affects the final results. According to this, it is possible to concluded that significative differences arise in the evaluation of the elastic properties that could be assigned to the uncertainties of the methods. However, this significance does not appear in the results of the probabilistic simulation.
Sentiment Analysis of Patients' Opinions in Healthcare using Lexicon-based Method

The rapid increase in technology made people across the world use social networking sites to express their opinions on a topic, product or service. The success of a healthcare service directly depends on its users. If a majority of users like the service then it is a success otherwise, the service needs to be improvised. For improvising the service, the users' opinions need to be analyzed. Manually extracting and analyzing the content present on the web is a tedious task. This gave rise to a new research area called Sentiment Analysis. It is otherwise known as opinion mining. It is being used by many health organizations to make effective decisions on their service. This paper presents the sentiment analysis of patients' opinions on hospitals which is mainly used to improve healthcare service. This is implemented using a lexicon-based methodology to analyze the sentiment.
Automatic Collision-Free Trajectory Generation for Collaborative Robotic Car-Painting

This paper investigates the problem of collaborative robotic car-painting using a team of industrial manipulators that can be heterogeneous. Given the CAD model of the car, a collection of heterogeneous articulated robotic arms, and their corresponding fixed base positions on the factory floor/ceiling, the objective is to generate a collection of joint trajectories for each robot in a computationally efficient manner such that the car body can be painted by the nozzles attached to the arms while collisions during the painting process are avoided. Our solution to this computationally intensive collaborative coverage path planning relies on decoupling the collision avoidance from the coverage path planning by exploiting the inherent two-dimensional structure of the problem. In particular, our algorithm relies on partitioning the reachable space of the forearms of these robots, projecting the resulting volumes of intersection on the sides and the top of the car body, and performing the coverage planning on the resulting projected volumes. Simulation results on several industrial arms that are collaboratively painting a Ford Motor Company F-150 truck demonstrate the effectiveness of our proposed solution.
FSC-Set: Counting, Localization of Football Supporters Crowd in the Stadiums

Counting the number of people in a crowd has gained attention in the last decade. Due to its benefit to many applications such as crowd behavior analysis, crowd management, and video surveillance systems, etc. Counting crowded scenes, like stadiums, represents a challenging task due to the inherent occlusions and density of the crowd inside and outside the stadiums. Finding a pattern to control thousands of people and counting them is a challenging task. With the introduction of Convolutional Neural Networks (CNN), enables performing this task with acceptable performance. The accuracy of a CNN-based method is related to the size of data used for training. The availability of the dataset is sparse. In particular, there is no dataset in the literature that can be used for training applications for crowd scene. This paper proposes two main contributions including a new dataset for crowd counting, and a CNN-based method for counting the number of people and generating the crowd density maps. The proposed dataset for Football Supporters Crowd (FSC-Set) is composed of 6000 annotated images (manually) of different types of scenes that contain thousands of people gathering in or around the stadiums. FSC-Set contains more than 1.5 Million individuals. The collected images are captured under varying Fields of Views (FOV), illuminations, resolutions, and scales. The proposed dataset can also be utilized for other applications, such as individual’s localization and face detection as well as team recognition from supporter images. Further, we propose a CNN-based method named FSCNet for crowd counting exploiting context-aware attention, spatial-wise attention, and channel-wise attention modules. The proposed method is evaluated on our established FSC-Set and other existing datasets then compared to state-of-the-art methods. The obtained results show satisfactory performances on all the datasets. The dataset is made publicly available and can be requested using the following link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/fscrowd-dataset/</uri>
A Case for Studying Naturalistic Eye and Head Movements in Virtual Environments

Using head mounted displays (HMDs) in conjunction with virtual reality (VR), vision researchers are able to capture more naturalistic vision in an experimentally controlled setting. Namely, eye movements can be accurately tracked as they occur in concert with head movements as subjects navigate virtual environments. A benefit of this approach is that, unlike other mobile eye tracking (ET) set-ups in unconstrained settings, the experimenter has precise control over the location and timing of stimulus presentation, making it easier to compare findings between HMD studies and those that use monitor displays, which account for the bulk of previous work in eye movement research and vision sciences more generally. Here, a visual discrimination paradigm is presented as a proof of concept to demonstrate the applicability of collecting eye and head tracking data from an HMD in VR for vision research. The current work's contribution is 3-fold: firstly, results demonstrating both the strengths and the weaknesses of recording and classifying eye and head tracking data in VR, secondly, a highly flexible graphical user interface (GUI) used to generate the current experiment, is offered to lower the software development start-up cost of future researchers transitioning to a VR space, and finally, the dataset analyzed here of behavioral, eye and head tracking data synchronized with environmental variables from a task specifically designed to elicit a variety of eye and head movements could be an asset in testing future eye movement classification algorithms.
Detection of illicit cryptomining using network metadata

Abstract Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims’ computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between compromised systems and remote mining pools using the de facto standard protocol Stratum. While prior research primarily focused on endpoint-based detection of in-browser mining, in this paper, we address network-based detection of cryptomining malware in general. We propose XMR-Ray, a machine learning detector using novel features based on reconstructing the Stratum protocol from raw NetFlow records. Our detector is trained offline using only mining traffic and does not require privacy-sensitive normal network traffic, which facilitates its adoption and integration.In our experiments, XMR-Ray attained 98.94% detection rate at 0.05% false alarm rate, outperforming the closest competitor. Our evaluation furthermore demonstrates that it reliably detects previously unseen mining pools, is robust against common obfuscation techniques such as encryption and proxies, and is applicable to mining in the browser or by compiled binaries. Finally, by deploying our detector in a large university network, we show its effectiveness in protecting real-world systems.
Fake News Detection on Social Media using Machine Learning Techniques

Web based life administrations, as Facebook and Twitter, Renren, Instagram, and linkedin have recently become an enormous and persistent supply of day by day news. These stages give a huge number of clients and give numerous administrations, for example, content arrangement and distributing. Not all distributed information via internet based medium is dependable and exact. Numerous individuals attempt to distribute fake and mistaken news so as to control general conclusion. Counterfeit news might be intentionally made to advance money related, political and public premiums, and can lead to unsafe effects on people convictions and choices.. In this paper we examine different systems for recognizing counterfeit information via internet based networking medium. Our point is to locate a dependable and right model that arranges a given article as fake or genuine. For identification of fake articles we use machine learning algorithms.
A Modified Whale Optimization Algorithm and Its Application in Seismic Inversion Problem

The whale optimization algorithm (WOA) is a popular swarm intelligence algorithm which simulates the hunting behavior of humpback whales. WOA has the deficiency of easily falling into the local optimal solutions. In order to overcome the weakness of the WOA, a modified variant of WOA called OCDWOA is proposed. There are four main operators introduced into the OCDWOA to enhance the search performance of WOA. The operators include opposition-based learning method, nonlinear parameter design, density peak clustering strategy, and differential evolution. The proposed algorithm is tested on 19 optimization benchmark functions and a seismic inversion problem. OCDWOA is compared with the classical WOA and three typical variants of WOA. The results demonstrate that OCDWOA outperforms the compared algorithms in terms of obtaining the global optimal solution.