Reviewerly/exHarmony huggingface dataset:
Multistage temporal convolution transformer for action segmentation

This paper addresses fully supervised action segmentation. Transformers have been shown to have large model capacity and powerful sequence modeling abilities, and hence seem quite suitable for capturing action grammar in videos. However, their performance in video understanding still lags behind that of temporal convolutional networks, or ConvNets for short. We hypothesize that this is because: (i) ConvNets tend to generalize better than Transformers, and (ii) Transformer's large model capacity requires significantly larger training datasets than existing action segmentation benchmarks. We specify a new hybrid model, TCTr, that combines the strengths from both frameworks. TCTr seamlessly unifies depth-wise convolution and self-attention in a principled manner. Also, TCTr addresses the Transformer's quadratic computational and memory complexity in the sequence length by learning how to adaptively estimate attention from local temporal neighborhoods, instead of all frames. Our experiments show that TCTr significantly outperforms the state of the art on the Breakfast, GTEA, and 50Salads datasets.
Changes in university students’ behavioral intention to learn online throughout the COVID-19: Insights for online teaching in the post-pandemic era

Many researchers investigated university students' behavioural intention to undertake online courses during COVID. However, few examined how students' intention might change throughout COVID by incorporating their learning capability and approaches. The universities in China went through a process from lockdown in February to reopening in September 2020. It provided a unique context for university students in China to experience emergent online learning for approximately six months before returning to normal face-to-face or blended learning on campus. The researchers conducted a questionnaire survey among 193 Chinese university students to investigate the changes in their behavioral intention to learn online throughout COVID. Additionally, the researchers explored the relationships between the participants' behavioral intention and the factors of learning capability in general, application of specific online learning strategies, online course engagement levels, and academic performance. It was found that the participants' intention to study online significantly increased during COVID and then slightly decreased after the university reopened. The participants' intention of online learning after COVID was predicted by their prior intention, learning capability, application of online learning strategies, and online course engagement. The participants' perceptions about online learning revealed that, when choosing future course delivery modes, they would a) reflect on their own disposition, capability, and needs, b) compare different learning modes, and c) examine course quality and teachers' competency. The participants also shared advice regarding their expectation of future online courses which may help shape university educators' pedagogical practices and provide insights for university online and blended course delivery from learners' perspectives.The online version contains supplementary material available at 10.1007/s10639-022-11320-0.
The Positive Effect of Attention Module in Few-Shot Learning for Plant Disease Recognition

Few-shot learning is good solution for plant disease recognition which can generalize to new categories by using few samples. However, the features extracted from few shots are limited. Attention is a technique for focusing on the significant features which can help to obtain better feature representation. In this work, we use a naive metric-based few-shot learning network as the baseline method, exploit the effect of different kinds of attention module: channel attention, spatial attention and hybrid attention. In experiments, we choose the representative modules of each attention category to show their effects in few-shot learning paradigm: SE, ASPP, CBAM and Triplet Attention. We conduct experiments with two data settings of PlantVillage, and illustrate the usage these attention modules in Residual Networks. The results indicate that the different attention modules can improve recognition accuracy to varying degrees. Attention can be used as effective improvement of feature representation under few-shot condition.
An improved entity recognition approach to cyber-social knowledge provision of intellectual property using a CRF-LSTM model

With the development of cutting-edge IT technologies, e.g. Big Data, Knowledge Engineering, etc., traditional Intellectual Property (IP) services have depicted high redundancy and low efficiency during management of such large-scale of data. Recent advancement of Artificial Intelligence (AI) and Deep Learning (DL) models has been accelerating relevant research activities being investigated on Knowledge Graph (KG) schemes and applications in different domains, such as medical services, social media, etc. However, when IP services and their cyber-social provision are taken into account, relevant approaches suffer from unbalanced labels against training results, and inappropriate evaluation metrics not well reflecting the impact of the unbalance. In this paper, a deep learning model combining Conditional Random Field and Bidirectional LSTM has been proposed, in order to achieve named entity recognition with unbalanced labels. An adaptive metric, G-Score was introduced to compare the fitting ability of models by evaluating the gap between Precision and Recall. According to the results, the proposed model can effectively recognize the potential named entities with outperformance over other relevant models.
Applying artificial intelligence techniques for predicting the environment, social, and governance (ESG) pillar score based on balance sheet and income statement data: A case of non-financial companies of USA, UK, and Germany

Due to globalization, environment, social, and governance (ESG) issues have gained importance over the last few decades. ESG is a worldwide issue, which clarifies that organizations throughout the world are lacking in contribution to the environment, society, and corporate governance characteristics for sustainable development. The problem of ESG spread over all stakeholders needs to be addressed. In this regard, rating agencies also have a close eye on ESG issues and have developed the methodology of score that aims to provide disclosure on ESG metrics which, in return, help investors and asset managers better differentiate between responsible and irresponsible companies. The ESG score has become an important tool among asset managers but is highly questioned in terms of reliability. The study objective was to develop machine learning algorithms to assess how balance sheet and income statement data impact the Thomson Reuters ESG score for non-financial public companies of USA, UK, and Germany from 2008 to 2020. In addition, the study also has an objective to assess which machine learning (ML) algorithm better predicts the ESG score using structural data, that is, return on assets (ROA), return on equity (ROE), earning per share (EPS), earnings before interest and taxes (EBIT), dividend yield, and net sales. The results concluded that balance sheet and income statement data are critical in explaining the ESG score, and the ANN algorithm outperforms with minimum RMSE and MAE values. All in all, the results of the study, based on the concept of artificial intelligence, bring suggestion for improvement to regulatory bodies, researchers, academia, practitioners, publicly listed companies around the globe, and last but not the least to the US, UK, and Germany markets. Moreover, it also provides suggestions for up-to-date compliance of ESG-relevant activities for boosting the firm performance.
A Education Tracking Approach Using Maps and Geo-location Method

In this study an approach is proposed to implement the tracking and selection of teachers based on maps and geo-location. The system consists of individual login for teachers and students. Students will have to input the Course, Subject and Topic that they wish to study, the app will then search and show the list of teachers that match the main criteria and nearby to the user&#x2019;s location along with other important information such as their rating, preferred subjects and rate. You can then select a teacher from the given list and choose the timing you want teacher to arrive at your provided location. This paper will focus on android operating system and will be great system for learning preferred topic at user&#x2019;s choice of location and time. This application is designed for delivering consistency and affordability quality education at your doorstep. In future, the proposed approach will be realized in real-time.
A transfer learning approach based on gradient boosting machine for diagnosis of Alzheimer’s disease

Early detection of Alzheimer's disease (AD) during the Mild Cognitive Impairment (MCI) stage could enable effective intervention to slow down disease progression. Computer-aided diagnosis of AD relies on a sufficient amount of biomarker data. When this requirement is not fulfilled, transfer learning can be used to transfer knowledge from a source domain with more amount of labeled data than available in the desired target domain. In this study, an instance-based transfer learning framework is presented based on the gradient boosting machine (GBM). In GBM, a sequence of base learners is built, and each learner focuses on the errors (residuals) of the previous learner. In our transfer learning version of GBM (TrGB), a weighting mechanism based on the residuals of the base learners is defined for the source instances. Consequently, instances with different distribution than the target data will have a lower impact on the target learner. The proposed weighting scheme aims to transfer as much information as possible from the source domain while avoiding negative transfer. The target data in this study was obtained from the Mount Sinai dataset which is collected and processed in a collaborative 5-year project at the Mount Sinai Medical Center. The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was used as the source domain. The experimental results showed that the proposed TrGB algorithm could improve the classification accuracy by 1.5 and 4.5% for CN vs. MCI and multiclass classification, respectively, as compared to the conventional methods. Also, using the TrGB model and transferred knowledge from the CN vs. AD classification of the source domain, the average score of early MCI vs. late MCI classification improved by 5%.
Choose your own future: the sociotechnical imaginaries of virtual reality

Virtual Reality has been heralded variously as the next steppingstone in technological innovation, a utopian 'empathy-machine' and a dystopian addictive technology. Using critical discourse analysis, we explore the types of narratives underlying this global attention and the ideological values, beliefs and interests therein. We contribute to the critical marketing literature by demonstrating how an examination of sociotechnical imaginaries reveals the ways in which the market mediates the reception of new technologies and the kinds of worlds these technologies bring about. Through an interactive 'choose your own adventure' narrative, we bring these imaginaries into relief and invite readers to navigate alternative potential futures for VR. The data underpinning the narrative highlight the role of marketers and marketing in shaping our social, political and economic reality.
Distribution of High-Sensitivity Cardiac Troponin and N-Terminal Pro–Brain Natriuretic Peptide in Healthy Transgender People

Importance Sex-specific differences in the commonly used cardiac biomarkers high-sensitivity cardiac troponin (hs-cTn) and N-terminal pro–brain natriuretic peptide (NT-proBNP) are apparent. There is an absence of medical literature delineating the concentration differences for these biomarkers in transgender individuals without cardiac disease. Objective To determine the distribution of hs-cTn and NT-proBNP in healthy transgender people. Design, Setting, and Participants In this cross-sectional prospective study, healthy transgender individuals prescribed testosterone or estradiol for 12 months or more were recruited from internal medicine and primary care clinics that specialize in transgender medical care between November 1, 2017, and July 1, 2018. Exposures Testosterone or estradiol for 12 months. Main Outcomes and Measures Concentrations for hs-cTnI (troponin I), hs-cTnT (troponin T), and NT-proBNP were measured. Results Transgender people prescribed testosterone (n = 79; mean [SD] age, 28.8 [7.8] years) or estrogen (n = 93; mean [SD] age, 35.1 [11.7] years) were recruited. The concentration of hs-cTn was significantly higher in transgender men relative to transgender women. For Abbott hs-cTnI levels, the median (IQR) concentration observed in transgender men and women was 0.9 (0.6-1.7) ng/L and 0.6 (0.3-1.0) ng/L, respectively. Results were similar across 2 additional hs-cTn assays. In contrast, NT-proBNP level was higher in transgender women. The median (IQR) NT-proBNP concentration was significantly higher in transgender women ( 49 [32-86] ng/L) than in transgender men (17 [13-27] ng/L). Conclusions and Relevance Findings of this cross-sectional study suggest that the differences in concentration for hs-cTn and NT-proBNP between transgender men and women were similar to what is observed between cisgender men and women. Sex hormones, rather than sex assigned at birth, may be a stronger driver of the observed concentration differences between healthy men and women for biomarkers of cardiac disease.
Towards a Definition of Hate Speech—With a Focus on Online Contexts

As legislators and platforms tackle the challenge of suppressing hate speech online, questions about its definition remain unresolved. In this review we discuss three issues: What are the main challenges encountered when defining hate speech? What alternatives are there for the definition of hate speech? What is the relationship between the nature and scope of the definition and its operationability? By tracing both efforts to regulate and to define hate speech in legal, paralegal, and tech platform contexts, we arrive at four possible modes of definition: teleological, pure consequentialist, formal, and consensus or relativist definitions. We suggest the need for a definition where hate speech encompasses those speech acts that tend towards certain ethically proscribed ends, which are destructive in terms of their consequences, and express certain ideas that are transgressions of specific ethical norms. [Media: see text]
Preferential sorption of polysaccharides on mackinawite: A chemometrics approach

Marine sediments represent the most important sink for natural organic matter (NOM) across geological time spans. Strong associations between minerals, including iron oxides, and organic matter reaching the seafloor play a fundamental role in this preservation and have been known for some decades. Despite the importance of this protective mechanism in the balances of the global carbon budget, very little is known about the affinity of NOM for reduced iron species such as mackinawite (FeS) in the anoxic layers of sediment. In this study, equilibrium partition coefficients (Kd) for three types of NOM (soil extract, corn leaf extract and plankton extract) on FeS were determined through batch sorption experiments. Models were then built via multiple linear regression (MLR) and partial least squares regression (PLSR) in order to predict sorption Kd's based on the chemical characteristics of the NOM. Investigation of the PLSR regression coefficients indicates that functional groups characteristic of polysaccharides are the greatest positive predictors of NOM sorption onto FeS at marine sediment porewater pH. This conclusion was independently verified by analyses of NOM FTIR spectra pre- and post-sorption. PLSR outperformed MLR with a lower root mean square error of prediction (RMSEP) of 53.4 L/kg compared to 64.0 L/kg. To our knowledge, this research presents a novel machine-learning approach to the quantitative modelling of NOM sorption to minerals found in anoxic marine environments.
Recurrence of a class of quantum Markov chains on trees

The problem of recurrence for quantum Markov chains on trees (QMCT), is more subtle than for 1D quantum Markov chains (QMC); it involves infinitely many rays due to the exponential growth of ramifications on the Cayley trees and their relevant constraints. We study criteria for recurrence of QMCT based on the correlations functions and boundary conditions. These represent a bridge between recurrence and phase transitions. Furthermore, we illustrate our results through an Ising model with competing interactions.
ICARUS-Q: Integrated control and readout unit for scalable quantum processors

We present a control and measurement setup for superconducting qubits based on the Xilinx 16-channel radio-frequency system-on-chip (RFSoC) device. The proposed setup consists of four parts: multiple RFSoC boards, a setup to synchronize every digital to analog converter (DAC) and analog to digital converter (ADC) channel across multiple boards, a low-noise direct current supply for tuning the qubit frequency, and cloud access for remotely performing experiments. We also designed the setup to be free of physical mixers. The RFSoC boards directly generate microwave pulses using sixteen DAC channels up to the third Nyquist zone, which are directly sampled by its eight ADC channels between the fifth and the ninth zones.
A text classification method based on LSTM and graph attention network

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Quantum Hamiltonian complexity in thermal equilibrium

The physical properties of a quantum many-body system in thermal equilibrium are determined by its partition function and free energy. Here we study the computational complexity of approximating these quantities for n-qubit local Hamiltonians. First, we report a classical algorithm with poly(n) runtime, which approximates the free energy of a given 2-local Hamiltonian provided that it satisfies a certain denseness condition. Our algorithm contributes to a body of work investigating the hardness of approximation for difficult optimization problems. Specifically, this extends existing efficient approximation algorithms for dense instances of the ground energy of 2-local quantum Hamiltonians and the free energy of classical Ising models. Second, we establish polynomial-time equivalence between the problem of approximating the free energy of local Hamiltonians and several other natural tasks ubiquitous in condensed-matter physics and quantum computing, such as the problem of approximating the number of input states accepted by a polynomial-size quantum circuit. These results suggest that the simulation of quantum many-body systems in thermal equilibrium may precisely capture the complexity of a broad family of computational problems that have yet to be defined or characterized in terms of known complexity classes. Finally, we summarize state-of-the-art classical and quantum algorithms for approximating the free energy and show how to improve their runtime and memory footprint. A quantum many-body system’s equilibrium behaviour is described by its partition function, which is hard to compute. Now it has been shown that the easier task of finding an approximation could define a distinct class of computational problems.
Research on the evaluation system of new energy development decision in Dunhuang city based on analytic hierarchy process

Dunhuang is rich in new energy resources such as solar energy, biomass energy and wind energy, which have great development prospects. However, due to the lack of the objective and scientific evaluation system for the development and utilization of new energy resources in Dunhuang, there have been different opinions and views on the selection of new energy fields that need to be developed first and the development sequence, which is not helpful to the scientific decision-making of new energy development. Aiming to solve this problem, this paper begins from the research status of new energy industry in Dunhuang, takes amount of available resources, land acquisition area, GDP contribution, environmental friendliness, investment cost and policy support as evaluation indicators, and takes biomass power generation, solar thermal utilization, photovoltaic power generation and wind power generation as development targets. The evaluation index system of new energy in Dunhuang is constructed by using the analytic hierarchy process, along with the optimal mode of new energy development in Dunhuang is studied. The study shows that in the evaluation index system of new energy development in Dunhuang, In the evaluation index system, the order of the influence of each index on the weight of the research objectives is the amount of available resources (0.3976) > GDP contribution (0.2684) > investment cost (0.1545) > policy support (0.0876) > environmental friendliness (0.0568) > land acquisition area (0.0350). In the development target layer, the order of the influence of each development factor on the weight of the research object is photovoltaic power generation (0.3535) > solar thermal utilization (0.2525) > wind power generation (0.2306) > biomass power generation (0. 1634). Therefore, Dunhuang should focus on the development of photovoltaic power generation industry. The results provide a abstract reference for the development of new energy in Dunhuang.
AugFCOS: Augmented fully convolutional one-stage object detection network

As a pioneering work of introducing the idea of full convolutional network into the field of object detection, the fully convolutional one-stage object detection network (FCOS) has the advantage of excellent performance with low memory overhead. However, there are certain problems with FCOS that merit more research: the centerness quality assessment loss does not decrease during the late training stage, and its adaptive training sample selection (ATSS) relies heavily on the hyperparameter. To solve the aforementioned problems, we propose a novel object detection network, named augmented fully convolutional one-stage object detection network (AugFCOS). First of all, we propose an improved dynamic optimization loss (DOL) to mitigate the impact of the original centerness loss not decreasing. Then, a Robust Training Sample Selection (RTSS) is proposed to get rid of the dependence of hyper-parameter in ATSS of FCOS. Finally, a novel mixed attention feature pyramid network (MAFPN) is presented to enhance the multi-scale representation ability of feature pyramid network (FPN) and further improve the ability of multi-scale detection. The experimental results on MS COCO demonstrate the effectiveness of our proposed AugFCOS, where AugFCOS achieves approximate 2.0% to 2.9% increase when compared with ATSS and FCOS.