Reviewerly/exHarmony huggingface dataset:
A Novel Ensemble Learning Paradigm for Medical Diagnosis With Imbalanced Data

With the help of machine learning (ML) techniques, the possible errors made by the pathologists and physicians, such as those caused by inexperience, fatigue, stress and so on can be avoided, and the medical data can be examined in a shorter time and in a more detailed manner.However, while the conventional ML techniques, such as classification, achieved excellent performance in classification accuracy when applied in medical diagnoses, they have a fatal shortcoming of poor performance since the imbalanced dataset, especially for the detection of the minority category.To tackle the shortcomings of conventional classification approaches, this study proposes a novel ensemble learning paradigm for medical diagnosis with imbalanced data, which consists of three phases: data pre-processing, training base classifier and final ensemble.In the first data pre-processing phase, we introduce the extension of Synthetic Minority Oversampling Technique (SMOTE) by integrating it with cross-validated committees filter (CVCF) technique, which can not only synthesize the minority sample and thereby balance the input instances, but also filter the noisy examples so as to perform well in the process of classification.In the classification phase, we introduce ensemble support vector machine (ESVM) classification technique, which were constructed by multiple diversity structures of SVM classifiers and thus has the advantages of strong generalization performance and classification precision.Additionally, in the last phase of the final ensemble strategy, we introduce the weighted majority voting strategy and introduce simulated annealing genetic algorithm (SAGA) to optimize the weight vector and thereby enhance the overall classification performance.The efficiency of our proposed ensemble learning method was tested on nine imbalanced medical datasets and the experimental results clearly indicate that the proposed ensemble learning paradigm outperforms other state-of-the-art classification models.Promisingly, our proposed ensemble learning paradigm can effectively facilitate medical decision making for physicians.
Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments

In this paper, we present an on-board visionbased approach for avoidance of moving obstacles in dynamic environments.Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles.Robust collision avoidance is achieved by formulating a chance-constrained model predictive controller (CC-MPC) to ensure that the collision probability between the micro aerial vehicle (MAV) and each moving obstacle is below a specified threshold.The method takes into account MAV dynamics, state estimation and obstacle sensing uncertainties.The proposed approach is implemented on a quadrotor equipped with a stereo camera and is tested in a variety of environments, showing effective on-line collision avoidance of moving obstacles.
Blockchain-based human resource management practices for mitigating skills and competencies gap in workforce

Skills gap between company needs and competencies occupied by the workforce can be the source of inefficiencies. The purpose of this research is to develop a blockchain-based human resource (HR) framework to match the needs from the company and workforce competencies This framework will help Corporate Training Centre to standardized the competencies which then used by HR Department to develop the training material. In order to get valid information regarding skills that are needed from the company, we develop a prototype based on Blockchain. Hence, blockchain-based HRM is built to improve the quality of workforce competency in an organization. The current organizations are struggling to fulfil the needs of the workforce in accordance with industry quality standards. Therefore, this will help all parties to create a consensus between the needs of the industry with the labour market. Corporate Training Centre through the competent institution will be the mediator or intermediary to unite the information from companies, training institutions, and Professional Certification Institutions. As a result, in the long term, the needs of the workforce with the qualification required by the company in such industries will always fit the current situation. Blockchain helps to process the information and data needed by each party so that the connection between parties will be assisted efficiently and effectively.
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction

A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs.This comes at a significant computational cost.On the other hand, sequence labeling approaches for OpenIE are much faster, but worse in extraction quality.In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10× faster.This is achieved through a novel Iterative Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling task.We improve its performance further by applying coverage (soft) constraints on the grid at training time.Moreover, on observing that the best OpenIE systems falter at handling coordination structures, our OpenIE system also incorporates a new coordination analyzer built with the same IGL architecture.This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12.3 pts improvement in F1 over previous analyzers.Our OpenIE system, OpenIE6 1 , beats the previous systems by as much as 4 pts in F1, while being much faster.
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

Systems for story generation are asked to produce plausible and enjoyable stories given an input context.This task is underspecified, as a vast number of diverse stories can originate from a single input.The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text.To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community.Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models.We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them.Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews.We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.
Revisiting the simulation of quantum Turing machines by quantum circuits

Yao's 1995 publication ‘Quantum circuit complexity’ in Proceedings of the 34th Annual IEEE Symposium on Foundations of Computer Science , pp. 352–361, proved that quantum Turing machines and quantum circuits are polynomially equivalent computational models: t ≥ n steps of a quantum Turing machine running on an input of length n can be simulated by a uniformly generated family of quantum circuits with size quadratic in t , and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time. We revisit the simulation of quantum Turing machines with uniformly generated quantum circuits, which is the more challenging of the two simulation tasks, and present a variation on the simulation method employed by Yao together with an analysis of it. This analysis reveals that the simulation of quantum Turing machines can be performed by quantum circuits having depth linear in t , rather than quadratic depth, and can be extended to variants of quantum Turing machines, such as ones having multi-dimensional tapes. Our analysis is based on an extension of method described by Arright, Nesme and Werner in 2011 in Journal of Computer and System Sciences 77 , 372–378. ( doi:10.1016/j.jcss.2010.05.004 ), that allows for the localization of causal unitary evolutions.
PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure

Research into the area of multiparty dialog has grown considerably over recent years.We present the Molweni dataset 1 , a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog.Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances.We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions.Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; (Asher et al., 2016)) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing.Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F 1 on Molweni's questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.
Cross-Layer Attention Network for Small Object Detection in Remote Sensing Imagery

In recent years, despite the tremendous progresses of object detection, small object detection has always been a challenge in the field of remote sensing. The main reason is that small objects cover few features that are easily lost during down-sampling. In this article, we propose a cross-layer attention network aiming to obtain stronger features of small objects for better detection. Specifically, we designed an up-sampling and down-sampling feature pyramid to obtain richer context information by bidirectionally fusing deep and shallow features, as well as skipping connections. Moreover, a cross-layer attention module is designed to obtain the nonlocal association of small objects in each layer, and further strengthen its representation ability through cross-layer integration and balance. Extensive experiments on the publicly available datasets (DIOR dataset and NWPUVHR-10 dataset) and the self-assembled datasets (SDOTA dataset and SDD dataset) show the excellent performance of our method compared with other detectors. Moreover, our method achieved 74.3% mAP on the public DIOR dataset without any tricks.
What is Core and What Future Holds for Blockchain Technologies and Cryptocurrencies: A Bibliometric Analysis

A new era awaits the development of cryptocurrency and blockchain technologies. This study highlights the influential and conceptual aspects of blockchain and cryptocurrency literature with bibliometric analysis. There are 1965 related documents from 2015 and 2020, represent various structures and technologies, cryptocurrencies, and blockchain applications. We have deployed “bibliometrix 3.0”, the r-package, and VOS viewer to analyze key literature aspects. IEEE Access is the core journal for cryptocurrency and blockchain publications. The University of Cagliari, Notreported, and Peking University are key affiliations, and the USA, China, and India are core countries for literary research. H. Wang is the core author with the central theme of security of blockchain technologies. It is a set basis for conceptual aspects, core, and future research streams and themes. The study proposes three research streams: the structure of cryptocurrencies and implications of blockchain technologies, privacy, security management of data and information, and development of optimal information systems. The study further segregates themes into highly centralized and motor themes that are also core themes. Topics related to Consensus protocols, proof of work, distribution ledger technology, blockchain, and cryptocurrency procedures and structure comes under core themes. Highly developed and emerging themes are considered as a stepping stone for future directions. Underlying topics are related to fintech, Islamic finance, valuations, and dynamics of cryptocurrencies.
Creating Collaborative Augmented Reality Experiences for Industry 4.0 Training and Assistance Applications: Performance Evaluation in the Shipyard of the Future

Industrial Augmented Reality (IAR) is one of the key technologies pointed out by the Industry 4.0 paradigm as a tool for improving industrial processes and for maximizing worker efficiency. Training and assistance are two of the most popular IAR-enabled applications, since they may significantly facilitate, support, and optimize production and assembly tasks in industrial environments. This article presents an IAR collaborative application developed jointly by Navantia, one of the biggest European shipbuilders, and the University of A Coruña (Spain). The analysis, design, and implementation of such an IAR application are described thoroughly so as to enable future developers to create similar IAR applications. The IAR application is based on the Microsoft HoloLens smart glasses and is able to assist and to guide shipyard operators during their training and in assembly tasks. The proposed IAR application embeds a novel collaborative protocol that allows operators to visualize and interact in a synchronized way with the same virtual content. Thus, all operators that share an IAR experience see each virtual object positioned at the same physical spot and in the same state. The collaborative application is first evaluated and optimized in terms of packet communications delay and anchor transmission latency, and then, its validation in a shipyard workshop by Navantia’s operators is presented. The performance results show fast response times for regular packets (less than 5 ms), low interference rates in the 5 GHz band, and an anchor transmission latency of up to 30 s. Regarding the validation tests, they allow for obtaining useful insights and feedback from the industrial operators, as well as clear guidelines that will help future developers to face the challenges that will arise when creating the next generation of IAR applications.
Influences of the environmental factors on the intention to adopt cloud based accounting information system among SMEs in Jordan

The objective of this study is to examine the role of external factors including MP, CP, and NP on the intention to adopt Cloud Based Accounting Information System (CB-AIS). The study proposes a theoretical framework based on institutional theory (INT). The data were collected from small and medium sized enterprise (SMEs) operating in Jordan. A total of 600 questionnaires were distributed to selected SMEs and only 142 were returned and used for the analysis. The empirical data were analyzed using the PLS-SEM modelling. The findings showed that MP, CP, and NP had significant direct associations with the CB-AIS intention to adopt. The results provide important insights to managers, researchers and policymakers to help them understand the importance of CB-AIS adopting to enhance firm performance.
Education According to the Fourth Industrial Industrial Revolution

The current study falls in line with the contemporary effort aiming towards raising the societal awareness about the impacts of 4th Industrial Revolution (IR 4.0) in education. The study aims to reveal the impact of IR 4.0 on the dimensions: control learning, learning opportunities, Instructional activities and social implications. To achieve the aim, a questionnaire was designed including 30 items that were distributed into the previous four dimensions and was applied on 77 pre-service science teachers at Sohar University in Sultanate of Oman in the academic year 2019/2020. Findings showed variations in the pre-service science teachers’ perceptions about implications of IR 4.0 on education. They believed that block-chain, Cloud Computing and cyber security will be used in a wide range in the future to improve learning opportunities and keep student’s data and activities for a long time. On the other hand, they referred that the teaching-learning processes will occur with no values and low level of students-teachers interactions. Also, they predicted that robots and machines will work instead of humans even in educational jobs in the future. According to the findings, the study suggested to implement significant transformations in instructional programs, curricula, learning environment, liquid instructional skills and teachers-students roles to deal with IR 4.0 technologies and products.
The Impact of Feature Importance Methods on the Interpretation of Defect Classifiers

Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. 2) The computed feature importance ranks by the studied CA methods exhibit a strong agreement including the features reported at top-1 and top-3 ranks for a given dataset and classifier, while even the commonly used CS methods yield vastly different feature importance ranks. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods.
One-class graph neural networks for anomaly detection in attributed networks

Nowadays, graph-structured data are increasingly used to model complex systems. Meanwhile, detecting anomalies from graph has become a vital research problem of pressing societal concerns. Anomaly detection is an unsupervised learning task of identifying rare data that differ from the majority. As one of the dominant anomaly detection algorithms, one-class support vector machine has been widely used to detect outliers. However, those traditional anomaly detection methods lost their effectiveness in graph data. Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data. In this work, we propose one-class graph neural network (OCGNN), a one-class classification framework for graph anomaly detection. OCGNN is designed to combine the powerful representation ability of graph neural networks along with the classical one-class objective. Compared with other baselines, OCGNN achieves significant improvements in extensive experiments.
Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.
Recognition of Imbalanced Epileptic EEG Signals by a Graph-Based Extreme Learning Machine

Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.