Reviewerly/exHarmony huggingface dataset:
Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives

Latelyproposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks.However, these systems typically implement the disambiguation of words in a document almost independently, underutilizing sense and word dependency in context.In this paper, we convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similaritybased Sense Aware Context Exploitation (SACE) architecture.Meanwhile, we enhance the context embedding learning with selected sentences from the same document, rather than utilizing only the sentence where each ambiguous word appears.Experiments on both English and multilingual WSD datasets have shown the effectiveness of our approach, surpassing previous state-of-the-art by large margins (3.7% and 1.2% respectively), especially on few-shot (14.3%) and zero-shot (35.9%) scenarios.
A Framework for Blockchain Technology Including Features

Blockchain is the foundation of Bitcoin has recently paid wide attention. Blockchain serves as a non-convertible account book that allows decentralized transactions. Research has shown that the potential of the manufacturing industry in the blockchain is credible because of the strategic use of blockchain involved investments and the business basically starts-up businesses that are contributing to the industries. This systematic review focuses on the diffusion of blockchain technology within supply chains, and great care was taken in selecting search terms. However, the authors acknowledge that their choice of terms may have excluded certain blockchain articles from this review. In this paper, we present an overview of blockchain technology in which four main pillars of this technology has been introduced and for more understanding technology behind Blockchain has been discussed.
Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.
ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates

This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms.From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained.The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden index criterion and the criterion that sets a target value for the false positive fraction.For the Bayesian methods, we provide tools for assessing model fit via posterior predictive checks, while the model choice can be carried out via several information criteria.Numerical and graphical outputs are provided for all methods.This is the only package implementing Bayesian procedures for ROC curves.
A novel strategy to develop deep learning for image super-resolution using original ultra-high-resolution computed tomography images of lung as training dataset

To improve the image quality of inflated fixed cadaveric human lungs by utilizing ultra-high-resolution computed tomography (U-HRCT) as a training dataset for super-resolution processing using deep learning (SR-DL). Image data of nine cadaveric human lungs were acquired using U-HRCT. Three different matrix images of U-HRCT images were obtained with two acquisition modes: normal mode (512-matrix image) and super-high-resolution mode (1024- and 2048-matrix image). SR-DL used 512- and 1024-matrix images as training data for deep learning. The virtual 2048-matrix images were acquired by applying SR-DL to the 1024-matrix images. Three independent observers scored normal anatomical structures and abnormal computed tomography (CT) findings of both types of 2048-matrix images on a 3-point scale compared to 1024-matrix images. The image noise values were quantitatively calculated. Moreover, the edge rise distance (ERD) and edge rise slope (ERS) were also calculated using the CT attenuation profile to evaluate margin sharpness. The virtual 2048-matrix images significantly improved visualization of normal anatomical structures and abnormal CT findings, except for consolidation and nodules, compared with the conventional 2048-matrix images (p < 0.01). Quantitative noise values were significantly lower in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.001). ERD was significantly shorter in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01). ERS was significantly higher in the virtual 2048-matrix images than in the conventional 2048-matrix images (p < 0.01). SR-DL using original U-HRCT images as a training dataset might be a promising tool for image enhancement in terms of margin sharpness and image noise reduction. By applying trained SR-DL to U-HRCT SHR mode images, virtual ultra-high-resolution images were obtained which surpassed the image quality of unmodified SHR mode images.
importance of speaking in English as a foreign language between skillful and thoughtful competencies: studying sociolinguistics perspectives

Speaking in a foreign language requires competence in the language skills itself and mindfulness so that the content of the speech is easier to reach and by the interlocutor. To understand the relationship between skillful and thoughtful in speaking English, we conducted a series of data collection with the help of an electronic engine based on Google Scholar data. We target these data in several international journal publications that discuss these two variables. After the data has been collected, it is then analyzed by involving a coding system and in-depth evaluation under a descriptive qualitative study. Before drawing data conclusions, we ensure that the data results meet reasonable and reliable standards. Based on the discussion of the findings, we can conclude that speaking in English as a foreign language requires skillful and thoughtful competence so that the interlocutor can easily understand the contents of the conversation. Thus, this finding should be a meaningful input for many parties in learning English.
Fifteen Years of Continuous High-Resolution Borehole Strainmeter Measurements in Eastern Taiwan: An Overview and Perspectives

As one of the most sensitive instruments for deformation monitoring in geophysics, borehole strainmeter has the capability to record a large spectrum of tectonic and environmental signals. Sensors are usually deployed near active faults and volcanoes and provide high-resolution continuous recordings of seismic and aseismic signals, hydrological variations (rainfall, groundwater level) and natural hazards (tropical cyclones, landslides, tsunamis). On the occasion of the 50th anniversary of the installation of the first Sacks–Evertson borehole strainmeter, in central Japan, we present an overview of the major scientific contributions and advances enabled by borehole strainmeter measurements in Taiwan since their installation in the mid 2000s. We also propose a set of future research directions that address recent challenges in seismology, hydrology and crustal strain modeling.
Multicarrier Multiplexing Continuous-Variable Quantum Key Distribution at Terahertz Bands Under Indoor Environment and in Inter-Satellite Links Communication

We propose a continuous-variable quantum key distribution (CVQKD) scheme at terahertz (THz) bands based on multicarrier multiplexing (MCM) technology. In this scheme, multiple Gaussian modulated subcarriers are coupled to transmit multipath superposed thermal Gaussian states. At the receiver, optical discrete Fourier transform (ODFT) is used to demultiplex the received subcarriers, and the keys can be generated in parallel by homodyne detection and post processing. We analyze the security of the scheme against the optimal collective Gaussian attack under indoor environment and in inter-satellite links respectively. Results indicate that by using MCM technology, each subchannel may be affected by crosstalk, resulting in slight shortening of the maximal transmission distance while the total secret key rate can be greatly increased. We also verify the feasibility of higher key rate and longer distance THz-QKD by using MCM technology in inter-satellite links communication. We expect this work will provide an efficient path to build a global quantum communication network.
(Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys

Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data.Yet even human annotation of social media content does not always capture "stance" as measured by public opinion polls.We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data.Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets.We find that recall is high for both "Pro" and "Anti" stance classifications but precision is variable in a number of cases.We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators.By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.
Easy Particle Swarm Optimization for Nonlinear Constrained Optimization Problems

Particle swarm optimization (PSO) is a popular stochastic approach for solving practical optimal problems from industries due to its effective performance and few hyperparameters. Nonlinear constrained optimization (NCO) problems frequently cause multiple optimal regions and can cause many infeasible regions in the search space. The state-of-the-art approaches for handling the infeasible regions generated by problems’ constraints either block particles’ paths or penalize NCO problems’ objective values based on the standard updating velocity formula. The standard updating velocity formula introduces difficulties for particles in searching the undiscovered optimal solutions separated by infeasible regions and being mutually restrained on directions by social and cognitive factors. Afterward, the particles cause premature convergence and difficulty searching the undiscovered optimal regions to improve their solutions. Observing the biological ant colony and inspired by lazy ant behavior, this study proposes an easy particle that simulates the lazy ant to diversify the moving direction. Finally, this study integrates the proposed easy particles with referenced PSO-based approaches for solving NCO problems. The experiment results show that the proposed easy particles can effectively reinforce exploration abilities and improve the performances of all referenced PSO-based algorithms to reduce the status of premature convergence in solving NCO problems.
Asset logging in the energy sector: a scalable blockchain-based data platform

Abstract Due to a steeply growing number of energy assets, the increasingly decentralized and segmented energy sector fuels the potential for new digital use cases. In this paper, we focus our attention on the application field of asset logging, which addresses the collection, documentation, and usage of relevant asset data for direct or later verification. We identified a number of promising use cases that so far have not been implemented; supposedly due to the lack of a suitable technical infrastructure. Besides the high degree of complexity associated with various stakeholders and the diversity of assets involved, the main challenge we found in asset logging use cases is to guarantee the tamper-resistance and integrity of the stored data while meeting scalability, addressing cost requirements, and protecting sensitive data. Against this backdrop, we present a blockchain-based platform and argue that it can meet all identified requirements. Our proposed technical solution hierarchically aggregates data in Merkle trees and leverages Merkle proofs for the efficient and privacy-preserving verification of data integrity, thereby ensuring scalability even for highly frequent data logging. By connecting all stakeholders and assets involved on the platform through bilateral and authenticated communication channels and adding a blockchain as a shared foundation of trust, we implement a wide range of asset logging use cases and provide the basis for leveraging platform effects in future use cases that build on verifiable data. Along with the technical aspects of our solution, we discuss the challenges of its practical implementation in the energy sector and the next steps for testing in a regulatory sandbox approach.
Hierarchical line segment matching for wide-baseline images via exploiting viewpoint robust local structure and geometric constraints

Line segment matching for wide-baseline images is challenging due to the significant viewpoint differences. In this study, we propose a hierarchical line segment matching method based on viewpoint robust local structure and geometric constraints. In our approach, line segments are paired and classified into three types representing heuristically those with different level of matchability: structured line pairs (S-LPs), unstructured line pairs (U-LPs), and individual line segments (I-LSs). Accordingly, we design a hierarchical matching framework that consists of three matching layers respectively corresponding to the above three types: in the first layer, robust local structures are constructed for S-LPs. We match the S-LPs by measuring local structure similarity (LSS). In the second layer, we build a topological descriptor-based constraint based on the S-LP matches and combine it with epipolar geometry-based constraints to select candidate matches for U-LPs, and then use LSS to further refine the matches. In the third layer, we estimate local homography for I-LSs to build constraints, to extract matches by exploiting the implicit region information of line pair matches. A pair of pre- and postprocessing algorithms, namely line segment merging and match reassignment, are performed before and after the matching procedure to overcome the negative effect of line segment fragmentation on the matching. Experimental results demonstrate that the proposed method performs better than state-of-the-art methods (with the largest improvement of 124.17% in terms of F-Measure over the best one among the compared methods).
Generative Feature Replay with Orthogonal Weight Modification for Continual Learning

The ability of intelligent agents to learn and remember multiple tasks sequentially is crucial to achieving artificial general intelligence. Many continual learning (CL) methods have been proposed to overcome catastrophic forgetting which results from non i.i.d data in the sequential learning of neural networks. In this paper we focus on class incremental learning, a challenging CL scenario. For this scenario, generative replay is a promising strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting. However, it is hard to train a generative model continually for relatively complex data. Based on recently proposed orthogonal weight modification (OWM) algorithm which can approximately keep previously learned feature invariant when learning new tasks, we propose to 1) replay penultimate layer feature with a generative model; 2) leverage a self-supervised auxiliary task to further enhance the stability of feature. Empirical results on several datasets show our method always achieves substantial improvement over powerful OWM while conventional generative replay always results in a negative effect. Meanwhile our method beats several strong baselines including one based on real data storage. In addition, we conduct experiments to study why our method is effective.
Deep-Learning-Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems

In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. To support long-term FD services, various power-saving strategies have been implemented. Among them, a reduced sampling rate is a common approach for an energy-efficient system in the real world. However, the performance of FD systems is diminished owing to low-resolution (LR) accelerometer signals. To improve the detection accuracy with LR accelerometer signals, several technical challenges must be considered, including mismatch of effective features and the degradation effects. In this work, a deep-learning-based accelerometer signal enhancement (ASE) model is proposed as a front-end processor to help typical LR-FD systems achieve better detection performance. The proposed ASE model based on a deep denoising convolutional autoencoder architecture reconstructs high-resolution (HR) signals from the LR signals by learning the relationship between the LR and HR signals. The results show that the FD system using support vector machine (SVM) and the proposed ASE model at an extremely low sampling rate (sampling rate < 2 Hz) achieved 97.34% and 90.52% accuracies in the SisFall and FallAllD data sets, respectively, while those without ASE models only achieved 95.92% and 87.47% accuracies in the SisFall and FallAllD data sets, respectively. The results also demonstrate that the proposed ASE mode can be suitably combined with deep-learning-based FD systems.
On the Commoditization of Artificial Intelligence

As artificial intelligence's potential and pervasiveness continue to increase, its strategic importance, effects, and management must be closely examined. Societies, governments, and business organizations need to view artificial intelligence (AI) technologies and their usage from an entirely different perspective. AI is poised to have a tremendous impact on every aspect of our lives. Therefore, it must have a broader view that transcends AI's technical capabilities and perceived value, including areas of AI's impact and influence. Nicholas G. Carr's seminal paper “IT Does not Matter (Carr, 2003 ) explained how IT's potential and ubiquity have increased, but IT's strategic importance has declined with time. AI is poised to meet the same fate as IT. In fact, the commoditization of AI has already begun. This paper presents the arguments to demonstrate that AI is moving rapidly in this direction. It also proposes an artificial intelligence-based organizational framework to gain value-added elements for lowering the impact of AI commoditization.
Evaluation and Classification Risks of Implementing Blockchain in the Drug Supply Chain with a New Hybrid Sorting Method

In blockchain technology, all registered information, from the place of production of the product to its point of sale, is recorded as permanent and unchangeable, and no intermediary has the ability to change the data of other members and even the data registered by them without public consensus. In this way, users can trust the accuracy of the data. Blockchain systems have a wide range of applications in the medical and health sectors, from creating an integrated system for recording and tracking patients’ medical records to creating transparency in the drug supply chain and medical supplies. However, implementing blockchain technology in the supply chain has limitations and sometimes has risks. In this study, BWM methods and VIKORSort have been used to classify the risks of implementing blockchain in the drug supply chain. The results show that cyberattacks, double spending, and immutability are very dangerous risks for implementation of blockchain technology in the drug supply chain. Therefore, the risks of blockchain technology implementation in the drug supply chain have been classified based on a literature review and opinions of the experts. The risks of blockchain technology implementation in the supply chain were determined from the literature review.
Predicting University Students’ Exam Performance Using a Model-Based Adaptive Fact-Learning System

Modern educational technology has the potential to support students to use their study time more effectively. Learning analytics can indicate relevant individual differences between learners, which adaptive learning systems can use to tailor the learning experience to individual learners. For fact learning, cognitive models of human memory are well suited to tracing learners’ acquisition and forgetting of knowledge over time. Such models have shown great promise in controlled laboratory studies. To work in realistic educational settings, however, they need to be easy to deploy and their adaptive components should be based on individual differences relevant to the educational context and outcomes. Here, we focus on predicting university students’ exam performance using a model-based adaptive fact-learning system. The data presented here indicate that the system provides tangible benefits to students in naturalistic settings. The model’s estimate of a learner’s rate of forgetting predicts overall grades and performance on individual exam questions. This encouraging case study highlights the value of model-based adaptive fact-learning systems in classrooms.