Reviewerly/exHarmony huggingface dataset:
Aspect-based sentiment analysis with graph convolution over syntactic dependencies

Aspect-based sentiment analysis is a natural language processing task whose aim is to automatically classify the sentiment associated with a specific aspect of a written text. In this study, we propose a novel model for aspect-based sentiment analysis, which exploits the dependency parse tree of a sentence using graph convolution to classify the sentiment of a given aspect. To evaluate this model in the domain of health and well-being, where this task is biased toward negative sentiment, we used a corpus of drug reviews. Specific aspects were grounded in the Unified Medical Language System, a large repository of inter-related biomedical concepts and the corresponding terminology. Our experiments demonstrated that graph convolution approach outperforms standard deep learning architectures on the task of aspect-based sentiment analysis. Moreover, graph convolution over dependency parse trees (F-score of 0.8179) outperforms the same approach over a flat sequence representation of sentences (F-score of 0.7332). These results bring the performance of sentiment analysis in health and well-being in line with the state of the art in other domains.
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes

We explore few-shot learning (FSL) for relation classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, [NOTA]), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distribution. To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset. This yields a new challenging benchmark for FSL-RC, on which state of the art models show poor performance. Next, we analyze classification schemes within the popular embedding-based nearest-neighbor approach for FSL, with respect to constraints they impose on the embedding space. Triggered by this analysis, we propose a novel classification scheme in which the NOTA category is represented as learned vectors, shown empirically to be an appealing option for FSL.
Joint categorical and ordinal learning for cancer grading in pathology images

Cancer grading in pathology image analysis is one of the most critical tasks since it is related to patient outcomes and treatment planning. Traditionally, it has been considered a categorical problem, ignoring the natural ordering among the cancer grades, i.e., the higher the grade is, the more aggressive it is, and the worse the outcome is. Herein, we propose a joint categorical and ordinal learning framework for cancer grading in pathology images. The approach simultaneously performs both categorical classification and ordinal classification and aims to leverage the distinctive features from the two tasks. Moreover, we propose a new loss function for the ordinal classification task that offers an improved contrast between the correctly classified examples and misclassified examples. The proposed method is evaluated on multiple collections of colorectal and prostate pathology images that underwent different acquisition and processing procedures. Both quantitative and qualitative assessments of the experimental results confirm the effectiveness and robustness of the proposed method in comparison to other competing methods. The results suggest that the proposed approach could permit improved histopathologic analysis of cancer grades in pathology images.
Object Detection Based on Fusion of Sparse Point Cloud and Image Information

With the rapid development of mobile robots, environmental perception based on a single sensor can hardly meet the task requirements of the robots for object detection and path planning in complex scenarios. In this article, an object detection fusion algorithm based on both the information of the LiDAR point cloud and the camera image is proposed. First, YOLOv4 is used to detect the objects in the image. Then, the point cloud is projected into the image, and the target point cloud is filtered out according to the range of the 2-D detection frame. The target point cloud is used to perform density clustering and generate the output of a bounding box with semantic labels. Meanwhile, the original point cloud data are processed by an improved four-neighborhood clustering algorithm based on the Euclidean distance and angle threshold to generate the output of another bounding box without semantic labels. Finally, the clusters obtained by different methods are fused and judged to produce the output of the final object detection results. The test using the KITTI dataset shows that the accuracy of the improved four-neighbor clustering algorithm is increased to 0.835. The final semantic segmentation results have an average positioning error of 0.033 and 0.073 m in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> - and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> -directions. The average angular error of the vehicle direction is 0.90°. Compared with the other two types of point cloud segmentation networks, our approach has the highest accuracy and sufficient real-time performance, which can reach 9.96 Hz in the experiment.
MommiNet: Mammographic Multi-view Mass Identification Networks

Most Deep Neural Networks (DNNs) based approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. In this paper, we present the first DNN-based tri-view mass identification approach (MommiNet), which can simultaneously perform end-to-end bilateral and ipsilateral analysis of mammogram images, and in turn can fully emulate the radiologists’ reading practice. Novel network architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views. Extensive experiments have been conducted on the public DDSM dataset and our in-house dataset, and state-of-the-art (SOTA) results have been obtained in terms of mammogram mass detection accuracy.
A comparison of novel metaheuristic algorithms on color aerial image multilevel thresholding

Color image thresholding is a well-known approach for image segmentation. An objective function, based on image entropy, is defined by threshold numbers and locations in the color histogram. Multiple classes of images can be created with multilevel thresholding. The main problem with thresholding techniques is to decide the threshold color values for each image. For a human operator, it is very hard to determine the specific threshold values of the images to be segmented. From this perspective, multilevel color image thresholding can be considered as an optimization problem that an algorithm should determine the optimum threshold values to obtain a perfectly segmented image. In recent years, metaheuristic algorithms become popular in several fields including image thresholding by their advantage of flexible structure. The motivation of this study relies on the fact that using same algorithm to solve any particular problem do not guarantee the best results as stated in no-free lunch theorem in optimization. Therefore, six novel nature inspired algorithms such as equilibrium optimization (EO), political optimizer (PO), turbulent flow of water-based optimization (TFWO), henry gas solubility optimization (HGSO), marine predators algorithm (MPA), and slime mould algorithm (SMA) are chosen to be compared by determining the multilevel color image thresholding values. These algorithms, which are used for the first time to solve this problem, are also compared statistically with extensive experiments. Aerial test images obtained by drones are used in the experiments. Kapur’s entropy and between-class variance (Otsu’s method) objectives are maximized by metaheuristic algorithms. Experimental results are evaluated with structural similarity (SSIM), peak-signal noise ratio (PSNR), blind/referenceless image spatial quality evaluator (BRISQUE), perception-based image quality evaluator (PIQE), natural image quality evaluator (NIQE), and computing CPU time consumption of the algorithms. Another problem of thresholding is that the question of how to compare the results objectively. Many image quality metrics may give incompatible results. Correlation analysis of the average ranking results show that image quality metrics used in this study are compatible to each other. Extensive experiments show that MPA and TFWO outperformed SMA, EO, PO and HGSO in terms of PSNR, SSIM, BRISQUE, PIQE, NIQE, and CPU time consumption for multilevel color aerial image thresholding.
Proposing a novel Dynamic AES for image encryption using a chaotic map key management approach

This article proposes a new method called Dynamic AES for image encryption based on the logistic chaotic map and the Advanced Encryption Standard (AES). Using the logistic mapping, the algorithm key is generated and combined at various stages with the information required for encryption. A special kind of Advanced Encryption Standard (AES) is used to encrypt images using the computing power at Galois field 28. The results of investigations and experiments show the superiority of the proposed algorithm over many image encryption algorithms over statistical and differential attacks. Obtaining the ideal value for the NPCR, near-ideal values for the UACI and Entropy, as well as the histogram and correlation studies of the adjacent pixels confirm the proper performance of the proposed algorithm.
Ring-Resonator-Based Coupling Architecture for Enhanced Connectivity in a Superconducting Multiqubit Network

Qubit coherence and gate fidelity are typically considered the two most important metrics for characterizing a quantum processor. An equally important metric is inter-qubit connectivity as it minimizes gate count and allows implementing algorithms efficiently with reduced error. However, inter-qubit connectivity in superconducting processors tends to be limited to nearest neighbour due to practical constraints in the physical realization. Here, we introduce a novel superconducting architecture that uses a ring resonator as a multi-path coupling element with the qubits uniformly distributed throughout its circumference. Our planar design provides significant enhancement in connectivity over state of the art superconducting processors without any additional fabrication complexity. We theoretically analyse the qubit connectivity and experimentally verify it in a device capable of supporting up to twelve qubits where each qubit can be connected to nine other qubits. Our concept is scalable, adaptable to other platforms and has the potential to significantly accelerate progress in quantum computing, annealing, simulations and error correction.
Learning and fusing multiple hidden substages for action quality assessment

Many of the existing methods for action quality assessment implement single-stage score regression networks that lack pertinence and rationality for the evaluation task. In this work, our target is to find a reasonable action quality assessment method for sports competitions that conforms to objective evaluation rules and field experience. To achieve this goal, three assessment scenarios, i.e., the overall-score-guided scenario, execution-score-guided scenario, and difficulty-level-based overall-score-guided scenario, are defined. A learning and fusion network of multiple hidden substages is proposed to assess athletic performance by segmenting videos into five substages by a temporal semantic segmentation. The feature of each video segment is extracted from the five feature backbone networks with shared weights, and a fully-connected-network-based hidden regression model is built to predict the score of each substage, fusing these scores into the overall score. We evaluate the proposed method on the UNLV-Diving dataset. The comparison results show that the proposed method based on objective evaluation rules of sports competitions outperforms the regression model directly trained on the overall score. The proposed multiple-substage network is more accurate than the single-stage score regression network and achieves state-of-the-art performance by leveraging objective evaluation rules and field experience that are beneficial for building an accurate and reasonable action quality assessment model.
Machine learning performance validation and training using a ‘perfect’ expert system

A method is proposed for generating application domain agnostic data for training and evaluating machine learning systems. The proposed method randomly generates an expert system network based upon user specified parameters. This expert system serves as a generic model of an unspecified phenomena. The expert system is run to determine the ideal output from a set of random inputs. These inputs and ideal output are used for training and testing a machine learning system. This allows a machine learning technology to be developed and tested without requiring compatible test data to be collected or before data collection as a proof-of-concept validation of system operations. It also allows systems to be tested without data error noise or with known levels of noise and with other perturbations, to facilitate analysis. It may also facilitate testing system security, adversarial attacks and conducting other types of research into machine learning systems.•Provides an application domain agnostic way to test machine learning technologies and facilitates the generalization of results.•Allows technologies to be tested with data with different characteristics without having to locate datasets that have these characteristics.•Utilizes randomly generated network to represent non-specific phenomena which can be used for training and testing machine learning techniques.
Federated Learning at the Network Edge: When Not All Nodes Are Created Equal

Under the federated learning paradigm, a set of nodes can cooperatively train a machine learning model with the help of a centralized server. Such a server is also tasked with assigning a weight to the information received from each node, and often also to drop too-slow nodes from the learning process. Both decisions have major impact on the resulting learning performance, and can interfere with each other in counter-in-tuitive ways. In this article, we focus on edge networking scenarios and investigate existing and novel approaches to such model-weighting and node-dropping decisions. Leveraging a set of realworld experiments, we find that popular, straightforward decision making approaches may yield poor performance, and that considering the quality of data in addition to its quantity can substantially improve learning.
Application of Improved Moth-Flame Optimization Algorithm for Robot Path Planning

Path planning is the focus and difficulty of research in the field of mobile robots, and it is the basis for further research and applications of robots. In order to obtain the global optimal path of the mobile robot, an improved moth-flame optimization (IMFO) algorithm is proposed in this paper. The IMFO features the following two improvement. Firstly, referring to the spotted hyena optimization (SHO) algorithm, the concept of historical best flame average is introduced to improve the moth-flame optimization (MFO) algorithm update law to increase the ability of the algorithm to jump out of the local optimum; Secondly, the quasi-opposition-based learning (QOBL) is used to perturb the location, increase the population diversity and improve the convergence rate of the algorithm. In order to evaluate the performance of the proposed algorithm, the IMFO algorithm is compared with three existing algorithms on three groups of different types of benchmark functions. The comparative results show that the IMFO algorithm is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Finally, the IMFO algorithm is applied to the path planning of the mobile robot, and computer simulations confirmed the algorithm’s effectiveness.
Cooperative MASS path planning for marine man overboard search

In a Man Overboard (MOB) incident, a quick and effective Search and Rescue (SAR) operation is crucial to increase the survival probability of the victim. Determining the search area and planning paths for the rescue ships are essential for efficient SAR operation. The search area's determination requires the prediction of the missing person, facing the challenges of lacking information about the accurate position and time of falling and the influence of environmental disturbances. Two main aims of path planning for SAR operation are quick arrival at the search area and coverage search with high cumulative Possibility of Success (POS). Many path planning algorithms have been proposed. Most of them aim at finding the shortest paths, which meet the goal of quick arrival. However, the path planning for maximizing POS of finding the person is still lacking. Besides, compared to an individual ship, a fleet of cooperative Maritime Autonomous Surface Ships (MASS) can significantly increase the POS and reduce SAR personnel's risk in a time-sensitive SAR operation. Therefore, in this paper, we propose a cooperative path planning framework to search for the missing person in a MOB incident using a fleet of fully autonomous MASS. The framework is divided into three modules, i.e., position prediction, target tracking, and coverage search. Firstly, the stochastic particle simulation method is used to predict the missing person's position considering the environment forecasting data, which determines the search area. Secondly, an adaptive greedy search algorithm is applied to tracking the drifting predicted area. Thirdly, the coverage search algorithm is designed with an adaptive neighborhood and evaluation function for increasing the cumulative POS in a limited time. Moreover, the path is smoothed by the Line-of-Sight algorithm and the kinematic interpolation method. Simulation experiments and sensitivity analysis are carried out to demonstrate the effectiveness of the proposed framework.
APPLE: Adaptive Planner Parameter Learning From Evaluative Feedback

Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however, fine-tuning of the system parameters by an expert is typically required before the system can navigate as expected. To alleviate this requirement, the recently-proposed Adaptive Planner Parameter Learning paradigm allows robots to \emph{learn} how to dynamically adjust planner parameters using a teleoperated demonstration or corrective interventions from non-expert users. However, these interaction modalities require users to take full control of the moving robot, which requires the users to be familiar with robot teleoperation. As an alternative, we introduce \textsc{apple}, Adaptive Planner Parameter Learning from \emph{Evaluative Feedback} (real-time, scalar-valued assessments of behavior), which represents a less-demanding modality of interaction. Simulated and physical experiments show \textsc{apple} can achieve better performance compared to the planner with static default parameters and even yield improvement over learned parameters from richer interaction modalities.
PPDDS: A Privacy-Preserving Disease Diagnosis Scheme Based on the Secure Mahalanobis Distance Evaluation Model

The development of Big Data and cloud computing has brought great progress to medical diagnosis and clinical services. However, as disease diagnosis technologies usually use lots of clinical medical data, patients’ privacy becomes increasingly important since the clinical results of medical data are much sensitive. In this article, to deal with the privacy preservation issue of patient’s medical data, we propose a privacy-preserving disease diagnosis scheme that is based on the Mahalanobis distance test, in which the role of the system consisting of query user (QU), aided cloud server (ACS), and classification cloud server (CCS) is to jointly calculate and protect the diagnosis data over the sensitive and outsourced medical data. In the diagnosis model, we outsource the clinical diagnosis process to be dealt with by the CCS, and then, the ACS can reduce the computational cost of the user side. We utilize the homomorphic re-encryption scheme to realize a secure computation over the outsourced medical data, and then, employ a secure multiplication (SM) protocol to implement the privacy-preserving Mahalanobis distance to output the disease diagnosis. Concretely, we provide an extended SM algorithm to solve the problem of multiplication of two encrypted data, and a minimum value comparison algorithm over ciphertext for comparing the encrypted Mahalanobis distance. Finally, we give the experimental performance in real data sheets, and the experimental results indicate that our scheme provides a lower computational cost in practical diagnosis services.
Vietnamese sign language detection using Mediapipe

Sign language is the only way used in communication for deaf and dumb people who cannot hear and speak. Viet Nam has nearly 2.5 million people with hearing and speaking disabilities, while the number of sign language interpreters in Vietnam is tiny. The hearing impaired has the same need for normal communication, access to information, and public services such as hospitals as ordinary people. The lack of sign language interpreters and effective methods to assist ordinary people in communicating with the hearing impaired require a convenient tool that makes sign language friendly for everyone. This paper presents an implementation using a recurrent neural network (RNN) with a Mediapipe hand tracking framework for Sign Language Gesture Recognition. Training data is created from input video using Multi-Hand Tracking and deep learning model that can recognize gestures by Hand Landmark Features per frame with RNN training. The dataset contains gestures of the most common words in Vietnamese. This model produces good accurate results in word recognition.
Fog Model-Based Hyperspectral Image Defogging

Fog in hyperspectral images severely limits the visibility of imaging scene and reduces the image contrast, which has a negative effect on the following image interpretation. Defogging methods aim at restoring a high-quality image from the degraded image. Currently, most dehazing methods mainly depend on the atmospheric scattering model in computer vision and multispectral image communities. However, when these approaches are directly used to remove the fog from HSIs, they cannot produce satisfactory defogging performance. To alleviate this issue, we develop a novel fog model to achieve fog removal from hyperspectral images. First, a fog density map is calculated by differentiating the averaged bands falling into visible and infrared spectral ranges. Then, haze abundance in different spectral bands is estimated based on the pixel reflectance between two selected pixels with different haze levels. Finally, the high-quality hyperspectral image is restored by solving the defogging model. Experiments performed on a new benchmark created by ourselves demonstrate that the proposed method obtains favorable dehazing performance in contrast to other approaches in computer vision and remote sensing fields.