Reviewerly/exHarmony huggingface dataset:
Research on Named Entity Recognition Method of Metro On-Board Equipment Based on Multiheaded Self-Attention Mechanism and CNN-BiLSTM-CRF

Massive and complex unstructured fault text data will be generated during the operation of subway trains. A named entity recognition model of subway on-board equipment based on Multiheaded Self-attention mechanism and CNN-BiLSTM-CRF is proposed to address the issue of low recognition accuracy and incomplete recognition features of unstructured fault data named entity recognition task of subway on-board equipment: BiLSTM-CNN parallel network extracts context feature information and local attention information, respectively; In the MHA layer, the features learned from different dimensions are fused through the Multiheaded Self-attention mechanism, and the dependencies of various ranges in the sequence are captured to yield the internal structure information of the features. The conditional random field CRF is used to learn the internal relationship between tags to ensure their sequence. This model is tested with other named entity recognition models on the marked subway on-board fault data. The experimental results demonstrate that this model is able to recognize 10 kinds of labels in the dataset. Moreover, the recognition effect of each label has a good performance in the three evaluation indexes of P, R, and F1 score. Moreover, the weighted average evaluation indexes <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>Avg</mtext> <mo>−</mo> <mi>P</mi> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>Avg</mtext> <mo>−</mo> <mi>R</mi> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>Avg</mtext> <mo>−</mo> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> of 10 labels in this model reach the highest 95.39%, 95.48%, and 95.37%, which has high evaluation indexes and can be applied to the named entity recognition of Metro on-board equipment.
Symbolic aggregate approximation based data fusion model for dangerous driving behavior detection

Detecting dangerous driving behavior is of great significance for reducing the occurrence of traffic accidents, and very challenging as it is affected by multiple factors. However, the existing methods are merely suitable for detecting dangerous driving behavior caused by single factor. Therefore, this paper proposes a novel Symbolic Aggregate approXimation based Data Fusion model (SAX-DF) to fuse multi-source data and applies it to accurately detect dangerous driving behavior. The proposed method considers multiple influencing factors at the same time and analyzes the related information between these factors. Further, a Positive Danger Mapping (PDM) algorithm is designed to unify the direction of multiple influencing factors. To verify the performance of SAX-DF, several experiments are carried out on traffic detector data and weather data. The experimental results illustrate that the proposed method can more effectively detect the dangerous driving behavior compared with the benchmark methods.
3D Reservoir Computing with High Area Efficiency (5.12 TOPS/mm<sup>2</sup>) Implemented by 3D Dynamic Memristor Array for Temporal Signal Processing

In this work, we realized a three-dimensional (3D) reservoir computing (RC) by utilizing the I-V nonlinearity and short-term memory of the dynamic memristor in 4-layer vertical array. The cycle-to-cycle variation of the dynamic reservoir is improved by parallel memristor configuration. The dimensionality of the reservoir space is increased by input strategy design. After the hardware-software co-optimization, the proposed 3D RC system exhibits high recognition accuracy (90%), low energy consumption (~0.78 pJ /operation), and high area efficiency (5.12 TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ).
The effect of entrepreneurial orientation on innovation performance in the airport industry through learning orientation and strategic alignment

In a highly competitive market and global economy, innovation is often recognised as the pinnacle of success and one of the most critical components. Entrepreneurial orientation has been recognised as an effective development strategy to advance innovation performance. Nevertheless, innovation research in the airport industry through entrepreneurial orientation has not received adequate attention. In addition, the development of innovation requires a dynamic change in strategic alignment and learning capacity. Nonetheless, the literature still lacks studies explaining how entrepreneurial orientation influences innovation performance through strategic alignment and learning orientation in the airport industry. Therefore, the present study aimed to examine the relationship between entrepreneurial orientation and innovation performance through the mediating effect of strategic alignment and learning orientation in the Dubai airport. A questionnaire was distributed to 413 employees from three main departments. The research findings revealed a significant positive relationship between entrepreneurial orientation and innovation performance. The findings also exposed that strategic alignment and learning orientation mediate the above relationship. Theoretically, this study extends the dynamic capabilities theory by providing an empirical model explaining how to dynamically improve innovation performance in the airport through the entrepreneurial orientation mediated by strategic alignment and learning orientation. Practically, this study also introduces a model that assists the managers in developing employees' entrepreneurial capabilities and airport information technology (IT) alignment and learning that are important to improve airport innovation performance.
Deep Image-based Illumination Harmonization

Integrating a foreground object into a background scene with illumination harmonization is an important but challenging task in computer vision and augmented reality community. Existing methods mainly focus on foreground and background appearance consistency or the foreground object shadow generation, which rarely consider global appearance and illumination harmonization. In this paper, we formulate seamless illumination harmonization as an illumination exchange and aggregation problem. Specifically, we firstly apply a physically-based rendering method to construct a large-scale, high-quality dataset (named IH) for our task, which contains various types of foreground objects and background scenes with different lighting conditions. Then, we propose a deep image-based illumination harmonization GAN framework named DIH-GAN, which makes full use of a multi-scale attention mechanism and illumination exchange strategy to directly infer mapping relationship between the inserted foreground object and the corresponding background scene. Meanwhile, we also use adversarial learning strategy to further refine the illumination harmonization result. Our method can not only achieve harmonious appearance and illumination for the foreground object but also can generate compelling shadow cast by the foreground object. Comprehensive experiments on both our IH dataset and real-world images show that our proposed DIH-GAN provides a practical and effective solution for image-based object illumination harmonization editing, and validate the superiority of our method against state-of-the-art methods. Our IH dataset is available at https://github.com/zhongyunbao/Dataset.
HeiMorph at SIGMORPHON 2022 Shared Task on Morphological Acquisition Trajectories

This paper presents the submission by the HeiMorph team to the SIGMORPHON 2022 task 2 of Morphological Acquisition Trajectories.Across all experimental conditions, we have found no evidence for the so-called Ushaped development trajectory.Our submitted systems achieve an average test accuracies of 55.5% on Arabic, 67% on German and 73.38% on English.We found that, bigram hallucination provides better inferences only for English and Arabic and only when the number of hallucinations remains low.
Frustratingly Easy System Combination for Grammatical Error Correction

In this paper, we formulate system combination for grammatical error correction (GEC) as a simple machine learning task: binary classification.We demonstrate that with the right problem formulation, a simple logistic regression algorithm can be highly effective for combining GEC models.Our method successfully increases the F 0.5 score from the highest base GEC system by 4.2 points on the CoNLL-2014 test set and 7.2 points on the BEA-2019 test set.Furthermore, our method outperforms the state of the art by 4.0 points on the BEA-2019 test set, 1.2 points on the CoNLL-2014 test set with original annotation, and 3.4 points on the CoNLL-2014 test set with alternative annotation.We also show that our system combination generates better corrections with higher F 0.5 scores than the conventional ensemble. 1
BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer

Adapter modules enable modular and efficient zero-shot cross-lingual transfer, where current state-of-the-art adapter-based approaches learn specialized language adapters (LAs) for individual languages.In this work, we show that it is more effective to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular sourcetarget transfer direction.Our novel BAD-X adapter framework trades off some modularity of dedicated LAs for improved transfer performance: we demonstrate consistent gains in three standard downstream tasks, and for the majority of evaluated low-resource languages.
Mobile Augmented Reality Based on Multimodal Inputs for Experiential Learning

The power of mobile devices has been harnessed as a platform for augmented reality (AR) that has embarked on opportunities to combine more inputs called multimodal inputs.There has been little research into the multimodal inputs that combine emotion, speech and markers in a mobile AR learning environment.This study aims to propose a framework for a mobile AR learning system that incorporates the combination of multimodal inputs, namely emotion, image-based marker and speech in order to determine how such a combination can enhance the learning experience.The proposed framework integrates the multimodal inputs based on the decision tree and develops into a four-phase learning system based on Kolb's experiential learning model.To evaluate this learning system, 38 students were selected and divided into two groups for an experiment on vocabulary at a primary school.Quantitative findings showed better results concerning learning effectiveness, mental load, engagement, competency and challenge when the three multimodal inputs, speech, marker and emotion, were combined.Therefore, the proposed multimodal framework can be used as a guideline to develop a multimodal based AR application for learning environment by integrating multimodal inputs and Kolb's experiential learning model.
Rethinking the A in FAIR Data: Issues of Data Access and Accessibility in Research

The FAIR data principles are rapidly becoming a standard through which to assess responsible and reproducible research. In contrast to the requirements associated with the Interoperability principle, the requirements associated with the Accessibility principle are often assumed to be relatively straightforward to implement. Indeed, a variety of different tools assessing FAIR rely on the data being deposited in a trustworthy digital repository. In this paper we note that there is an implicit assumption that access to a repository is independent of where the user is geographically located. Using a virtual personal network (VPN) service we find that access to a set of web sites that underpin Open Science is variable from a set of 14 countries; either through connectivity issues (i.e., connections to download HTML being dropped) or through direct blocking (i.e., web servers sending 403 error codes). Many of the countries included in this study are already marginalized from Open Science discussions due to political issues or infrastructural challenges. This study clearly indicates that access to FAIR data resources is influenced by a range of geo-political factors. Given the volatile nature of politics and the slow pace of infrastructural investment, this is likely to continue to be an issue and indeed may grow. We propose that it is essential for discussions and implementations of FAIR to include awareness of these issues of accessibility. Without this awareness, the expansion of FAIR data may unintentionally reinforce current access inequities and research inequalities around the globe.
Human-cyber-physical system for operation in nuclear reactor possessing asymmetric multi-task learning-based predicting framework

This work proposes a novel architecture of a human-cyber-physical system (HCPS) to operate nuclear reactors in the context of next-generation artificial intelligence (NGAI) technologies. Developing the generation IV nuclear power plant requires NGAI to fulfill intelligent operation and thereby enhance reliability and safety. Nevertheless, the NGAI based on machine computing is limited in auto-cognition whereas humans are adept in information processing. Operation states prediction is served as a specific scene to investigate the key enabling technologies (KETs) in HCPS, where humans are elevated to a supervisory level for NGAI technologies. Humans interact with NGAI in two-stage which comprise the initial construction and later upgradation of an intelligent prediction framework. In this paper, asymmetric multitask learning (AMTL) is elaborated as a KET which is essential to implementing intelligent prediction in HCPS. AMTL simultaneously trains multi-tasks and permits asymmetric information transfer between tasks. The amount of transfer varies with inter-task relatedness and individual task losses, thereby preventing negative transfer and enhancing accuracy. Furthermore, the improved AMTL is adapted for the small and imbalanced samples of tasks. A sparse and directed regularisation graph is introduced to visualise the correlation between tasks, reflecting the physical mechanism as well. Finally, the improved AMTL is utilized to predict reactivity of a specific pressurised water reactor (PWR), which validates the efficiency and accuracy.
Strategic Speed Choice by High-Frequency Traders under Speed Bumps

We study how high-frequency traders (HFTs) strategically decide their speed level in a market with a random speed bump. If HFTs recognize the market impact of their speed decision, they perceive a wider bid-ask spread as an endogenous upward-sloping cost of being faster. We find that the speed elasticity of the bid-ask spread (slope of the endogenous cost function) negatively depends on the expected length of a speed bump since a longer delay makes market makers insensitive to HFTs' speed increment. Hence, speed bumps promote the investment of HFTs in high-speed technology by reducing the marginal cost of getting faster, undermining their intended purpose of protecting market makers. Depending on the expected length of a bump, an arms race among HFTs exhibits both complementarity and substitution. These findings explain the ambiguous empirical results regarding speed bumps and adverse selection for market makers.
A Proposal: For Emerging Gaps in Finding Firm Solutions for Cross Site Scripting Attacks on Web Applications

There are many web vulnerabilities and popular among them is Cross Site Scripting Attacks (XSS). The XSS vulnerability can go to the extent of intruding on an organization's data via its web application. The activities the hijacker performs during these XSS attacks are accessing user sessions, deleting, adding, and modifying the data of the websites. Additionally, as they have control over the web pages, they add malicious code to distort the user interface and stop further business activities. If an organization’s website is providing service across the globe, this would halt all the user transactions for many hours until the issue is resolved. The attackers would further proceed to access the organization's servers if the situation is not handled to stop the XSS attacks. These real- time scenarios explain the severity of the XSS attacks. Further implementing solutions to not face further attacks is still continuing. The reason for the quest to find better solutions is to avoid these XSS attacks, because the hackers are always finding various routes to hack these web applications. However, even after finding many solutions, these attacks are happening regularly. Hence it is necessary to discover the gap to find an appropriate solution even before any new XSS attack happens. This paper proposes a methodology to explore these gaps and solutions to an ongoing cross site scripting attacks.
Hierarchical Representation Learning for Attributed Networks

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, such as structural similarity, attribute similarity, etc. The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks, such as node classification and link prediction, benefits plenty of practical applications.
Online Deconvolution for Industrial Hyperspectral Imaging Systems

This paper proposes a hyperspectral image deconvolution algorithm for the online restoration of hyperspectral images as provided by wiskbroom and pushbroom scanning systems.We introduce a least-mean-squares (LMS)-based framework accounting for the convolution kernel non-causality and including non-quadratic (zero attracting and piece-wise constant) regularization terms.This results in the so-called sliding block regularized LMS (SBR-LMS) which maintains a linear complexity compatible with real-time processing in industrial applications.A model for the algorithm mean and mean-squares transient behavior is derived and the stability condition is studied.Experiments are conducted to assess the role of each hyper-parameter.A key feature of the proposed SBR-LMS is that it outperforms standard approaches in low SNR scenarios such as ultra-fast scanning.
Seeking Subjectivity in Visual Emotion Distribution Learning

Visual Emotion Analysis (VEA), which aims to predict people&#x2019;s emotions towards different visual stimuli, has become an attractive research topic recently. Rather than a single label classification task, it is more rational to regard VEA as a Label Distribution Learning (LDL) problem by voting from different individuals. Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process. In psychology, the <i>Object-Appraisal-Emotion</i> model has demonstrated that each individual&#x2019;s emotion is affected by his/her subjective appraisal, which is further formed by the affective memory. Inspired by this, we propose a novel <i>Subjectivity Appraise-and-Match Network (SAMNet)</i> to investigate the subjectivity in visual emotion distribution. To depict the diversity in crowd voting process, we first propose the <i>Subjectivity Appraising</i> with multiple branches, where each branch simulates the emotion evocation process of a specific individual. Specifically, we construct the affective memory with an attention-based mechanism to preserve each individual&#x2019;s unique emotional experience. A subjectivity loss is further proposed to guarantee the divergence between different individuals. Moreover, we propose the <i>Subjectivity Matching</i> with a matching loss, aiming at assigning unordered emotion labels to ordered individual predictions in a one-to-one correspondence with the Hungarian algorithm. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed SAMNet consistently outperforms the state-of-the-art methods. Ablation study verifies the effectiveness of our method and visualization proves its interpretability.
An Ontological Model based on Machine Learning for Predicting Breast Cancer

Breast cancer is mostly a female disease, but it may affect men as well even at a considerably lower percentage.An automated diagnosis system should be built for early detection because manual breast cancer diagnosis takes a long time.Doctors have lately achieved significant advances in the early identification and treatment of breast cancer in order to decrease the rate of mortality caused by the latter.Researchers, on the other hand, are analysing large amounts of complicated medical data by employing a combination of statistical and machine learning methodologies to assist clinicians in predicting breast cancer.Various machine learning approaches, including ontology-based Machine Learning methods, have lately played an essential role in medical science by building an automated system that can identify breast cancer.This study examines and evaluates the most popular machine learning algorithms, besides the ontological model based on Machine Learning.Among the classification methods investigated were Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, Artificial Neural Network, Random Forest, and k-Nearest Neighbours.The dataset utilized has 683 instances and is available for download from the Kaggle website.The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Precision, and Recall.The ontology model surpassed all machine learning techniques, according to the results.
Research on the Application of Blockchain Technology in Coal Supply Chain Finance

The implementation of measures to limit electricity consumption in many provinces of China has caused coal prices to rise irrationally, further aggravating the financing problems of small and medium-sized enterprises in the supply chain. Small and medium-sized enterprises lacking funds cannot effectively participate in the green transformation and development of the coal industry, which slows down the sustainable development process of the coal industry. Under the current background of low-carbon advocacy, blockchain technology can reasonably allocate resources and efficiently process information, thereby providing a solution for this financing problem. This paper first proposes a coal accounts receivable financing model based on blockchain technology, then builds a coal accounts receivable financing system dominated by ports through blockchain technology. Finally, the Stackelberg yield–benefit model is used to analyze the income function of each participant in the process of accounts receivable financing. The results show that the use of blockchain technology can reduce the financing condition of financial institutions and improve the maximum income of cooperative enterprises in the chain while solving the financing problems of small and medium-sized enterprises in the coal supply chain. This study provides practical significance and theoretical value for promoting the transformation and upgrading of coal enterprises and accelerating the opening of the sustainable development model of the coal industry.