Reviewerly/exHarmony huggingface dataset:
Receiver operating characteristic (ROC) curves: equivalences, beta model, and minimum distance estimation

Abstract Receiver operating characteristic (ROC) curves are used ubiquitously to evaluate scores, features, covariates or markers as potential predictors in binary problems. We characterize ROC curves from a probabilistic perspective and establish an equivalence between ROC curves and cumulative distribution functions (CDFs). These results support a subtle shift of paradigms in the statistical modelling of ROC curves, which we view as curve fitting. We propose the flexible two-parameter beta family for fitting CDFs to empirical ROC curves and derive the large sample distribution of minimum distance estimators in general parametric settings. In a range of empirical examples the beta family fits better than the classical binormal model, particularly under the vital constraint of the fitted curve being concave.
Deep Learning-based Cyber Security Solutions for Smart-City: Application and Review

This book chapter provides the readers hands-on experience about the deep learning algorithms and their use case for cyber security solutions in the smart city ecosystem. In this book chapter, the authors first describe the background about the smart city ecosystem, cyber security threats and deep learning algorithms. Subsequently, the authors describe the overview of popular architectures of deep learning algorithms with their applications for addressing various cyber security threats. In this study, the author talks about convolutional neural networks (CNNs), fully connected convolutional networks (FCNs), Recurrent Neural Network (RNN), Deep Belief Network (DBN), Boltzmann Machine, and Auto-encoders.
Can high-school students contribute to seismic risk mitigation? Lessons learned from the development of a crowd-sourced exposure database

On a global scale, one out of three people is exposed to earthquakes, and most injuries and deaths in case of seismic events are caused by the damage or collapse of residential buildings. Knowing the type and distribution of exposed assets, in particular buildings, is thus paramount for effective mitigation of disasters. Citizens' involvement in disaster risk reduction activities is constantly increasing by means of crowdsourced data collection, education/training and citizen science activities. In particular, schools have a central role in engaging young students and increasing risk awareness and preparedness. In this work, we explored whether students' participation in this kind of activities can lead to the improvement of current exposure datasets, while increasing citizens' risk-related understanding. In 2021, due to the restrictions imposed by the COVID-19 pandemic, the National Institute of Oceanography and Applied Geophysics - OGS started a new project to be deployed in fully remote mode, named "CEDAS: building CEnsus for seismic Damage ASsessment". The project consists in the collection and elaboration of crowdsourced data on main residential buildings typologies of northeastern Italy, a seismically active area which suffered consequences from strong past earthquakes. During the project, 170 high school students collected reports on more than 3200 buildings, performing a statistical analysis of theing results. The CEDAS project makes a first step beyond crowdsourcing activities and applies citizen science to exposure development. Results allow identifying the most common building typologies in the region and the challenges and opportunities associated with data collection and analysis. The experience collected during the CEDAS project shows that crowdsourcing and citizen science activities can contribute to both enhancing the exposure data available for the scientific community and increasing risk awareness among young students in the region.
Enhanced Image Captioning with Color Recognition Using Deep Learning Methods

Automatically describing the content of an image is an interesting and challenging task in artificial intelligence. In this paper, an enhanced image captioning model—including object detection, color analysis, and image captioning—is proposed to automatically generate the textual descriptions of images. In an encoder–decoder model for image captioning, VGG16 is used as an encoder and an LSTM (long short-term memory) network with attention is used as a decoder. In addition, Mask R-CNN with OpenCV is used for object detection and color analysis. The integration of the image caption and color recognition is then performed to provide better descriptive details of images. Moreover, the generated textual sentence is converted into speech. The validation results illustrate that the proposed method can provide more accurate description of images.
Eyelid gestures for people with motor impairments

Although eye-based interactions can be beneficial for people with motor impairments, they often rely on clunky or specialized equipment (e.g., stationary eye-trackers) and focus primarily on gaze and blinks. However, two eyelids can open and close in different orders and for different duration to form rich eyelid gestures. We take a first step to design, detect, and evaluate a set of eyelid gestures for people with motor impairments on mobile devices. We present an algorithm to detect nine eyelid gestures on smartphones in real time and evaluate it with 12 able-bodied people and 4 people with severe motor impairments in two studies. The results of the study with people with motor-impairments show that the algorithm can detect the gestures with .76 and .69 overall accuracy in user-dependent and user-independent evaluations. Furthermore, we design and evaluate a gesture mapping scheme for people with motor impairments to navigate mobile applications only using eyelid gestures. Finally, we discuss considerations for designing and using eyelid gestures for people with motor impairments.
In-situ recognition of hand gesture via Enhanced Xception based single-stage deep convolutional neural network

In-situ detection and recognition of hand gestures in an unconstrained environment is a crucial task in the field of machine vision and video recognition. Hand gesture recognizing task is perhaps more complicated, with varying lighting conditions and background. Robust, accurate hand detection and classification becomes necessary to facilitate the Graphical User Interface, Tele-surgery, etc. In this paper, a Convolutional Neural Network (CNN) based Hybrid Single Stage Recognition (Hybrid-SSR) framework is proposed for real-time hand gesture recognition. The time-efficient Enhanced Xception (E-Xception) CNN model is proposed for feature extraction. The framework is evaluated using three separate datasets (NUSHP-II, Senz-3D, and MITI-HD). The performance of the framework is evaluated for the range of IoU from 0.5 to 0.95. In comparison with the conventional single-stage hand gesture recognition system, the Hybrid-SSR model resulted in higher precision values (99.60 % on AP0.5, 97.80 % on AP0.75, and 88.20 % on AP0.5:0.95) for the MITI-HD dataset. The Hybrid-SSR model with Adam Optimizer surpasses the other optimization techniques. The proposed model achieved comparatively higher precision and recall values for focusing parameter (γ) = 2 and weighting parameter (α) = 0.5. The misclassification problem encountered by the conventional single-stage models is mitigated by implementing the focal loss function in the classification sub-network. The computation cost of Hybrid-SSR (12 ms) is significantly lower compared to other single-stage hand gesture recognition frameworks.
Automatic question generation and answer assessment for subjective examination

Automatic generation of questions and evaluating their answers is a highly challenging task in natural language processing and educational technology. This work focuses on generating subjective questions and also an evaluation system is suggested for assessing the answers. For generating the questionnaires, key-phrases are extracted from the course curriculum (syllabus). Next, based on the key-phrases, different types of subjective questions are generated. Finally, the evaluation of student’s responses is achieved using a multi-criteria-decision-making approach. It uses a set of model answers taken from different textbooks and subject experts to evaluate the answers. Multiple measures are used to assess the answers by comparing them with this model set. The results of the profound system reveal that the automated appraisal process can reduce the manual effort of the human.
Hyperparameter Tuning using GridsearchCV on The Comparison of The Activation Function of The ELM Method to The Classification of Pneumonia in Toddlers

Pneumonia is a disease that is susceptible to attack toddlers. According to data from the Ministry of Health, the cause of under-five deaths due to pneumonia is number 2 of all under-five deaths. In Kalimantan, forest fires are one of the causes of the high number of pneumonia cases. Knowing the symptoms of the disease is very important, considering that sufferers often do not know that they have been exposed to Pneumonia because the symptoms that appear are just ordinary pain. In this study, the classification of Pneumonia and Non- Pneumonia Cough was carried out based on symptom factors. The dataset used in this study is the Poly MTBS at the Martapura Timur Health Center. The classification method used in this research is Extreme Learning Machine (ELM). The classification process starts from SMOTE upsampling, this is done to balance the classes, because the amount of data between classes used is not balanced. Then hyper tuning the parameters is done using GridsearchCV on the hidden layer neurons, to determine the best parameters that will be used as recommendations in the classification process. At the classification stage using the ELM method by comparing the activation functions of Binary Sigmoid, Sin, Hard Limit, Triangular Basis, Radial Base, Linear, and Bipolar Sigmoid by comparing test datasets 90:10, 80:20, 70:30, 60:40, and 50:50. This study provides the best performance results on the use of the Triangular Base activation function with 86.36% accuracy, 85% precision, 100% recall and 92% F1 Score, training data ratio, and testing 90:10 and 3 hidden layer neurons.
Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia

A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: "Sehha", "Mawid", "Sehhaty", "Tetamman", "Tawakkalna", and "Tabaud". We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were "Tawakkalna" followed by "Tabaud", and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps' services and user experience, especially during health crises.
A weakly supervised model for the automated detection of adverse events using clinical notes

With clinical trials unable to detect all potential adverse reactions to drugs and medical devices prior to their release into the market, accurate post-market surveillance is critical to ensure their safety and efficacy. Electronic health records (EHR) contain rich observational patient data, making them a valuable source to actively monitor the safety of drugs and devices. While structured EHR data and spontaneous reporting systems often underreport the complexities of patient encounters and outcomes, free-text clinical notes offer greater detail about a patient's status. Previous studies have proposed machine learning methods to detect adverse events from clinical notes, but suffer from manually extracted features, reliance on costly hand-labeled data, and lack of validation on external datasets. To address these challenges, we develop a weakly-supervised machine learning framework for adverse event detection from unstructured clinical notes and evaluate it on insulin pump failure as a test case. Our model accurately detected cases of pump failure with 0.842 PR AUC on the holdout test set and 0.815 PR AUC when validated on an external dataset. Our approach allowed us to leverage a large dataset with far less hand-labeled data and can be easily transferred to additional adverse events for scalable post-market surveillance.
#iamhere: Collective Counterspeech and the Quest to Improve Online Discourse

This article examines the Facebook group #jagärhär, a Sweden-based collective of thousands of people who have made a regular practice of responding en masse to what they regard as hateful comments online. #jagärhar is one of the largest and best-organized collective efforts to respond directly to hatred online anywhere in the world. Drawing on data collected through ethnographic observation and interviews, the article explores two primary research questions: (1) how do the external counterspeech actions of group members work to counter hatred (and, sometimes, misinformation)? and (2) how do the internal practices of the group keep members engaged? I argue that instead of focusing their work on preventing future hateful speech (presumably by changing the minds or incentives of those who post it), #jagärhär members fight against its effects—attempting to lessen the impact of the hateful speech by hiding it in the comment threads, speaking to the “movable middle” rather than those posting hatred, and encouraging more counterspeech against it.
A machine learning method to predict the technology adoption of blockchain in Palestinian firms

Purpose The study aims to deliver a decision support system for business leaders to estimate the potential for effective technological adoption of the blockchain (TAB) with a machine learning approach. Design/methodology/approach This study uses a Bayesian network examination to develop an extrapolative system of decision support, highlighting the influential determinants that managers can employ to predict the TAB possibilities in their companies. Data were gathered from 167 SMEs in the largest industrial sectors in Palestine. Findings The results reveal perceived benefit and ease of use as the most influential determinants of the TAB. Originality/value This research is an initial effort to examine factors influencing TAB in the perspective of SMEs in Palestine using machine learning algorithms.
WITHDRAWN: Multimedia-based English teaching and practical system

In the era of Big Data, English teaching services incorporation and growth face the challenge of the vast volume, diversity, and unseen meaning of the data resources. Multimedia education has considerable realistic importance in the current situation. This calls for contemporary English teachers to learn the criteria and basic digital education operations and combine modern information technology with English teaching. Using a multimedia-based teaching strategy for teaching English has several drawbacks. Multimedia production is more costly than other types of production because it involves the use of many media. Multimedia production also necessitates the use of an electronic equipment, which can be rather expensive. In this paper, the multimedia-based English teaching model (METM) has been proposed to reduce student English learning difficulties. The proposed method METM uses cloud networking to address multimedia-based learning resources' security challenges and hardware capabilities in colleges and universities. The test results show that the multimedia network teaching paradigm is accurate and feasible. The suggested model for teaching increases the students' passion for learning and their practical English skills for the proper use of English. The numerical outcome of the paper is Student test analysis (97.6%), Performance analysis (93%), and feedback analysis (98.5%).
Network Assurance in Intent-Based Networking Data Centers with Machine Learning Techniques

Intent-Based Networking (IBN) is a recently proposed networking solution that allows networks to be configured and adapted autonomously according to the users' or operators' high-level intentions. However, a significant component of IBN is to assure that the network accurately and automatically deploys the intent throughout its lifecycle. To this end, in this study, we propose a network assurance solution for data center IBN networks. For the assurance model, we propose some specific data preparation procedures and Machine Learning (ML) models for the problem of time series forecasting. Specifically, we construct three main ML models that are based on the architecture of Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN). Our evaluation experiments, based on real data center Virtual Machine (VM) data traces, reveal the effectiveness of our methods in terms of CPU percentage usage prediction accuracy and speed. At the same time, our best-performing model can predict sufficiently far into the future with good accuracy.
Developing a generic framework for anomaly detection

The fusion of one-class classifiers (OCCs) has been shown to exhibit promising performance in a variety of machine learning applications. The ability to assess the similarity or correlation between the output of various OCCs is an important prerequisite for building of a meaningful OCCs ensemble. However, this aspect of the OCC fusion problem has been mostly ignored so far. In this paper, we propose a new method of constructing a fusion of OCCs with three contributions: (a) As a key contribution, enabling an OCC ensemble design using exclusively non anomalous samples, we propose a novel fitness function to evaluate the competency of OCCs without requiring samples from the anomalous class; (b) As a minor, but impactful contribution, we investigate alternative forms of score normalisation of OCCs, and identify a novel two-sided normalisation method as the best in coping with long tail non anomalous data distributions; (c) In the context of building our proposed OCC fusion system based on the weighted averaging approach, we find that the weights optimised using a particle swarm optimisation algorithm produce the most effective solution. We evaluate the merits of the proposed method on 15 benchmarking datasets from different application domains including medical, anti-spam and face spoofing detection. The comparison of the proposed approach with state-of-the-art methods alongside the statistical analysis confirm the effectiveness of the proposed model.
What kind of revolution? Thinking and rethinking educational technologies in the time of COVID-19

Background The transition to technology-mediated remote schooling during the COVID-19 pandemic represented a drastic shift in educational technologies’ function in K-12 settings. This theoretical paper sought to: (1) identify key developments in technology-use during the pandemic; (2) situate current events within the Learning Sciences’ evolving conceptualizations of educational technologies; and (3) outline how these developments should reframe our thinking about educational technologies.Methods The paper is structured along three sets of relations, intended to support analyses that go beyond determinist or instrumental depictions of educational technologies: education-technology, human-technology, and human-education.Findings I outline three key characteristics of educational technologies’ function during the pandemic: they were central to the grammar of schooling, their use was widespread across social contexts, and was need-driven rather than innovation-driven.Contribution Accordingly, the paper suggests reorienting existing conceptualizations of educational technologies: (i) rethinking learning—avoiding the portrayal of technologies as solutions to educational problems and examining how they reshape learning; (ii) rethinking context—attending more to how socio-cultural, political, and historical features inform technological affordances; (iii) rethinking teaching—emphasizing adults’ role in mediating the normative commitments underlying technology-use, particularly in light of the dominance of commercial platforms and tools.
BERT gated multi-window attention network for relation extraction

Entity relation extraction aims to identify the semantic relation between entity pairs in a sentence and is an important technical support for downstream tasks such as question answering systems and semantic searching. Existing relation extraction models mainly rely on neural networks to extract the semantic information of sentences, ignoring the critical role of important phrase information in relation extraction. For this problem, this paper proposes a relation extraction model based on BERT gated multi-window attention network (BERT-GMAN). The model first uses BERT to extract the semantic representation features of the sentence and its constraint information. Secondly, it constructs the key phrases extraction network to obtain multi-granularity phrase information and uses element-wise max pooling to select key phrases features. Thirdly, it adopts classification feature perception network to further filter and globally perceive key phrase feature to form the overall features of relation classification. Finally, it combines with Softmax classifier to perform relation extraction. The experimental results on the Semeval-2010 Task 8 dataset show that the performance of the model in this paper is further improved compared with the existing methods, and the F1-score reaches 90.25%.
Courier

With the advent of serverless computing in different domains, there is a growing need for dynamic adaption to handle diverse and heterogeneous functions. However, serverless computing is currently limited to homogeneous Function-as-a-Service (FaaS) deployments or simply FaaS Deployment (FaaSD) consisting of deployments of serverless functions using a FaaS platform in a region with certain memory configurations. Extending serverless computing to support Heterogeneous FaaS Deployments (HeteroFaaSDs) consisting of multiple FaaSDs with variable configurations (FaaS platform, region, and memory) and dynamically load balancing the invocations of the functions across these FaaSDs within a HeteroFaaSD can provide an optimal way for handling such serverless functions.
Comparison of Different Lossy Image Compression Techniques

Recently, the use of large volumes of image data in many applications like internet has been increasing rapidly. So, to make an effective use of storage space and also bandwidth of the network, image compression is required. We have two kinds of image compression - one is lossy and other is lossless image compression. Lossy image compression produces a compressed image where quality of the image is maintained with some data loss. Lossy compression is widely used compared to lossless compression. Here, three lossy image compression techniques - Discrete Cosine Transform(DCT), Singular Value Decomposition (SVD) and Discrete Wavelet Transform(DWT) are used to perform image compression. These techniques are compared using some performance measures such as Peak Signal-to- Noise Ratio(PSNR), Compression Ratio(CR), Structural Similarity Index Measure(SSIM) and Mean Square Error(MSE).