Reviewerly/exHarmony huggingface dataset:
Lightweight hierarchical residual feature fusion network for single-image super-resolution

In recent years, numerous lightweight convolution neural networks (CNNs) have made remarkable progress for single image super-resolution (SISR) and showed great power for image reconstruction under constrained resources. However, existing lightweight networks can not fully utilize the informative hierarchical features, which will lead to the degradation of network reconstruction. To alleviate this issue, we propose a hierarchical residual feature network named HRFFN. Specifically, we design an enhanced residual block (ERB) containing multiple mixed attention blocks (MABs) to boost the representative ability of the network. Compared with ordinary residual blocks, ERB can achieve better performance while reducing network parameters and computational complexity. To utilize more features from intermediate convolution layers, we introduce a hierarchical feature fusion strategy (HFFS) to efficiently fuse the detailed information from each ERB step by step. By fully utilizing the hierarchical details with this strategy, we can refine the hierarchical features more efficiently. Besides, we cooperate the global dense connection strategy (GDCS) and residual learning connection (RLC, at low, meditate, and high levels) to construct our HRFFN. By employing these strategies, we can maximize the utilization of hierarchical features with a slight increase in parameters. Comprehensive experiments show the superiority of our method on five benchmark datasets against other state-of-the-art methods, which achieves a comparable trade-off between visual quality and quantitative metrics.
Unknown Object Segmentation from Stereo Images

Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and severely limits the range of potential applications. Therefore, we propose a novel object instance segmentation approach that does not require any semantic or geometric information of the objects beforehand. In contrast to existing works, we do not explicitly use depth data as input, but rely on the insight that slight viewpoint changes, which for example are provided by stereo image pairs, are often sufficient to determine object boundaries and thus to segment objects. Focusing on the versatility of stereo sensors, we employ a transformer-based architecture that maps directly from the pair of input images to the object instances. This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map. In experiments in several different application domains, we show that our Instance Stereo Transformer (INSTR) algorithm outperforms current state-of-the-art methods that are based on depth maps. Training code and pretrained models are available at https://github.com/DLR-RM/instr.
Random vector functional link with ε-insensitive Huber loss function for biomedical data classification

Biomedical data classification has been a trending topic among researchers during the last decade. Biomedical datasets may contain several features noises. Hence, the conventional machine learning model cannot efficiently handle the presence of noise in datasets. Among the several machine learning model, the random vector functional link (RVFL) is one of the most popular and efficient models for task related to both classification and regression. Despite its excellent classification performance, its performance degrades while dealing with the datasets with noise. Researchers are searching for powerful models to minimize the influence of noise in datasets. Therefore, to enhance the classification ability of RVFL on noisy datasets, this paper suggests a novel random vector functional link with ε-insensitive Huber loss function (ε-HRVFL) for biomedical data classification problems.The optimization problem of ε-HRVFL is reformulated as strongly convex minimization problems with a simple function iterative approach to find solutions. To have a better understanding of the scope of the biomedical data classification problem and potential solutions, we conducted experiments with three different types of label noise in biomedical datasets as well as a few non-biomedical datasets. The classification accuracy of the proposed ε-HRVFL model is compared statistically using Friedman test with the support vector machine, extreme learning machine with radial basis function (RBF) and sigmoid activation functions and RVFL with RBF and sigmoid activation functions.For non-biomedical datasets, the proposed model showed the highest accuracy of 98.1332%. Moreover, for the biomedical datasets, the proposed model showed the best accuracy of 96.5229%. The proposed ε-HRVFL model with sigmoid activation function reveals the best mean ranks among the reported classifiers for both, biomedical and non-biomedical datasets.Numerical results show the applicability of the proposed ε-HRVFL model. In future, the proposed ε-HRVFL can be developed to solve multiclass biomedical data classification problems. Moreover, ε-insensitive asymmetric Huber loss function based RVFL model can be developed for dealing more efficiently with these noisy biomedical datasets.
Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation

Abstract A current mineral exploration focus is the development of tools to identify magmatic districts predisposed to host porphyry copper deposits. In this paper, we train and test four, common, supervised machine learning algorithms: logistic regression, support vector machines, artificial neural networks (ANN) and Random Forest to classify metallogenic ‘fertility’ in arc magmas based on whole-rock geochemistry. We outline pre-processing steps that can be used to mitigate against the undesirable characteristics of geochemical data (high multicollinearity, sparsity, missing values, class imbalance and compositional data effects) and therefore produce more meaningful results. We evaluate the classification accuracy of each supervised machine learning technique using a tenfold cross-validation technique and by testing the models on deposits unseen during the training process. This yields 81–83% accuracy for all classifiers, and receiver operating characteristic (ROC) curves have mean area under curve (AUC) scores of 87–89% indicating the probability of ranking a ‘fertile’ rock higher than an ‘unfertile’ rock. By contrast, bivariate classification schemes show much lower performance, demonstrating the value of classifying geochemical data in high dimension space. Principal component analysis suggests that porphyry-fertile magmas fractionate deep in the arc crust, and that calc-alkaline magmas associated with Cu-rich porphyries evolve deeper in the crust than more alkaline magmas linked with Au-rich porphyries. Feature analysis of the machine learning classifiers suggests that the most important parameters associated with fertile magmas are low Mn, high Al, high Sr, high K and listric REE patterns. These signatures further highlight the association of porphyry Cu deposits with hydrous arc magmas that undergo amphibole fractionation in the deep arc crust.
Improving complex knowledge base question answering via structural information learning

Responding to complex questions is one of the most difficult and valuable goals of KBQA. Current efforts mainly follow two approaches to extract the in-depth semantics of questions. Information retrieval-based methods tend to encode questions directly and ignore the explicit analysis of the question structure. Besides, although retaining the analysis ability of question structure, semantic parsing-based methods rely on the expensive query graph labels and suffer from sparse reward due to wrong explorations. To benefit from both sides, this paper proposes a novel semantic parsing model, structural information restraint (SIR) for KBQA. SIR applies structural information of questions for reinforcement-based path reasoning for the first time. Specifically, SIR synthesizes the dependency tree, constituency tree, and the first token to build a composited structural attention (StrucAtt) and realizes reasoning without expensive query graphs labels. Such an attention mechanism improves the efficiency of path reasoning by distinguishing different knowledge paths, based on the relevance between path features and question structure. In addition, we also design a type-assisted reward based on answer concepts (person, location, etc.) instead of simple variable types (string, number, etc.), which alleviates the sparse reward problem effectively. Besides, the experiment results clearly show that our model achieves SOTA on CWQ, CQ, and WQSP datasets.
Explainable deep learning in healthcare: A methodological survey from an attribution view

The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken. In this review, we focus on the interpretability of the DL models in healthcare. We start by introducing the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners in this field. Besides the methods' details, we also include a discussion of advantages and disadvantages of these methods and which scenarios each of them is suitable for, so that interested readers can know how to compare and choose among them for use. Moreover, we discuss how these methods, originally developed for solving general-domain problems, have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies. Overall, we hope this survey can help researchers and practitioners in both artificial intelligence and clinical fields understand what methods we have for enhancing the interpretability of their DL models and choose the optimal one accordingly. This article is categorized under: Cancer > Computational Models.
Deep learning prediction of axillary lymph node status using ultrasound images

To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images. In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014–12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not). Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS). It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with breast cancer.
Gas path component fault diagnosis of an industrial gas turbine under different load condition using online sequential extreme learning machine

One of the challenges that data-based gas path component monitoring systems are facing, is the poor performance in those operational conditions that are not considered in the training process. An approach could be retraining the model repeatedly with a large number of previous and fresh data from the scratch, which would require a lot of time and storage memory. To address this problem, a component fault diagnostic system based on the bank of online sequential extreme learning machines (OSELMs) are applied that can incrementally update with any number of new training samples. This system is adapted for a two-shaft industrial gas turbine that is used for power generation. Besides, an optimal set of measurements are selected as the input of the system using a hybrid approach based on the variable length genetic algorithm and extreme learning machine (VLGA-ELM). The performance of OSELM based diagnostic system is compared with several batch learning algorithms by analyzing the confusion matrix of all systems. The results show that the OSELM based approach reflects a promising compromise between the overall accuracy, false positive rate, F1-score and false negative rate, while having the least training time compared with the other systems.
Semantic Relatedness Enhanced Graph Network for aspect category sentiment analysis

As a variant problem of aspect-based sentiment analysis (ABSA), aspect category sentiment analysis (ACSA) aims to identify the aspect categories discussed in sentences and predict their sentiment polarities. However, most aspect-based sentiment analysis (ABSA) research focuses on predicting the sentiment polarities of given aspect categories or aspect terms explicitly discussed in sentences. In contrast, aspect categories are often discussed implicitly. Additionally, most of the research does not consider the relations between contextual words and aspect categories. This paper proposes a novel Semantic Relatedness-enhanced Graph Network (SRGN) model which integrates the semantic relatedness information through an Edge-gated Graph Convolutional Network (EGCN). We introduce an ontology-based approach and a distributional approach to calculate the semantic relatedness values between contextual words and aspect categories. EGCN with the capability to aggregate multi-channel edge features, is then applied to model the semantic relatedness values in a graphical structure. We also employ an aspect–context attention module to generate aspect-specific representations. The proposed SRGN is evaluated on five datasets constructed based on SemEval 2015, SemEval 2016 and MAMC-ACSA datasets. Experimental results indicate that our proposed model outperforms the baseline models in both accuracy and F1 score.
Accountable outsourcing data storage atop blockchain

Outsourcing data integrity auditing enables users to efficiently check whether their cloud data are stored intactly without retrieving them. However, a problem arises that how to hold the storage server accountable when the auditing results show that the data has been lost or tampered with. In this paper, we propose a blockchain-based accountable data outsourcing storage protocol, which enables users to obtain fine-grained compensation based on the damage of data. We provide an accurate and verifiable damage assessment method to achieve quantitative compensation. Specifically, the damage is assessed by the server and verified by smart contract. Furthermore, our protocol ensures the fairness of participants by deposit mechanism and smart contract. If any participant behaves dishonestly, it cannot obtain any benefit or even be punished. To reduce user's overhead, we propose a new provable data possession scheme, PDP in short, supporting outsourcing the calculation of tags. Moreover, we provide a detailed security proof for the proposed protocol as well. Finally, we evaluate the time cost of off-chain functions and the gas cost of on-chain operations for our protocol, which demonstrates that it is practical and suitable in reality.
Smart Data Analytics on COVID-19 Data

Advances in computers, information and networks has brought a digital cyber world to our daily lives. They have generated numerous digital things (or cyber entities), which have resided in the cyber world. Meanwhile, countless real things in the conventional physical, social and mental worlds have possessed cyber mappings (or cyber components) to have a cyber existence in cyber world. Consequently, cyberization has been an emerging trend forming the new cyber world and reforming conventional worlds towards cyber-enabled hyper-worlds. As such, cybermatics helps build systematic knowledge about new phenomena, behaviors, properties and practices in the cyberspace, cyberization and cyber-enabled hyper-worlds. Cybermatics is characterized by catching up with the human intelligence (e.g. intelligent sensing, making decision and control, etc.), as well as learning from the nature-inspired attributes (e.g., dynamics, self-adaptability, energy saving). As a cybermatics technique, smart data analytics helps filter out the noise data and produce valuable data. In this paper, we focus on smart data analytics on health data related to coronavirus disease 2019 (COVID-19). It builds temporal and demographic hierarchies, which capture characteristics of COVID-19 patients, to discover valuable knowledge and information about temporal-demographic characteristics of these patients. Evaluation on real-life COVID-19 epidemiological data demonstrates the practicality of our solution in conducting smart data analytics on COVID-19 data.
A Novel Mean-Shift Algorithm for Data Clustering

We propose a novel Mean-Shift method for data clustering, called Robust Mean-Shift (RMS).A new update equation for point iterates is proposed, mixing the ones of the standard Mean-Shift (MS) and the Blurring Mean-Shift (BMS).Despite its simplicity, the proposed method has not been studied so far.RMS can be set up in both a kernel-based and a nearest-neighbor (NN)-based fashion.Since the update rule of RMS is closer to BMS, the convergence of point iterates is conjectured based on the Chen's BMS convergence theorem.Experimental results on synthetic and real datasets show that RMS in several cases outperforms MS and BMS in the clustering task.In addition, RMS exhibits larger attraction basins than MS and BMS for identical parametrization; consequently, its kernel variant requires a lower aperture of the kernel function, and its NN variant a lower number of nearest neighbors compared to MS or BMS, to achieve optimal clustering results.In addition, the NN version of RMS does not need to specify a convergence threshold to stop the iterations, contrarily to the NN-BMS algorithm.
A systematic review of AR/VR in operations and supply chain management: maturity, current trends and future directions

Purpose This research aims to provide systematic insight into the current maturity of augmented reality (AR) and virtual reality (VR) in operations and supply chain management (OSCM), by analyzing the existing literature, contemporary concepts, data and gaps for future research directions. Design/methodology/approach This research uses information from existing literature regarding timeline trends, publishers, research design and method, industry engagement, geographic location, active authors and affiliated universities, concentrated topics, theories and maturity in its review. A collection of publishing databases from 1997 to 2021 were explored using the keywords “Augmented Reality,” “Virtual Reality” and “Operations” and/or “Supply Chain” in their title and abstract to examine which publications to be included. Based on the search, a total of 164 journal articles were selected, and information on a chain of variables was collected. Findings There has been a significant publication growth over the past 25 years regarding the adoption of AR/VR in OSCM. Key findings indicate that 52% of the publications were focused on manufacturing, with only 10% of the existing literature using background theories. AR/VR can be observed at the introduction and growth phase and have yet to reach their maturity. Furthermore, there is limited utilization of AR/VR as drivers in facilitating sustainable practices in OSCM by academics and practitioners, albeit a strong promise exists. Finally, the prospective applications of AR/VR toward post-COVID-19 supply chains recovery require special attention. Research limitations/implications This systematic review is limited to considering only academic articles available from Emerald, Elsevier, Taylor and Francis, Springer, Scopus, JSTOR and EBSCO containing the keyword parameters. Originality/value The study used a bibliometric review to identify the trends and maturity in the evolution of AR/VR in OSCM. This research provides a better understanding of current research practices and offers directions toward the adoption of AR/VR in OSCM.
Kinematic viscosity estimation of fuel oil with comparison of machine learning methods

It is known that one of the important parameters affecting the emission and performance values of the liquid fuels used in thermal machines is the viscosity value. Therefore, many different studies have been carried out on the determination of dynamic and kinematic viscosities of liquid fuels. In this study, the kinematic viscosity value of Fuel Oil 4 at a constant temperature of 100 °C was estimated using machine learning methods. Extreme Learning Machine (ELM), Multi-Layer Perceptron (MLP), and K Nearest Neighbor (K-nn) methods were used to perform kinematic viscosity estimations. Two different distance metrics are considered in the K-nn algorithm. The experimentally obtained water content, density and flash point properties of the fuel were used as input data for machine learning approaches. Thus, four different models were developed for the kinematic viscosity estimation of Fuel oil fuel. The success rates of the predictions were compared using the Mean Relative Error (MRE) and Mean Squared Error (MSE). As a result, it was seen that all of the methods discussed provide predictive ability in accordance with standard values and the best prediction data is provided by using ELM.