Reviewerly/exHarmony huggingface dataset:
Attention-Based Multilevel Co-Occurrence Graph Convolutional LSTM for 3-D Action Recognition

Action recognition is essential for many human-centered applications in the Internet of Things (IoT). Especially, in the Internet of Medical Things (IoMT), action recognition shows great importance in surgical assistance, patient monitoring, etc. Recently, 3-D skeleton sequence-based action recognition draws broad attention. It is a challenging task that needs effective modeling on intraframe skeleton representations and interframe temporal dynamics. Standard long short-term memory (LSTM)-based models are widely used for sequence modeling due to its long-term memory, yet they are unable to fully model the relationship between different body joints or persons to extract crucial co-occurrence features from different levels. To handle this shortcoming, we propose an attention-based multilevel co-occurrence graph convolutional LSTM (AMCGC-LSTM). By integrating graph convolutional networks (GCNs) into LSTM, the proposed model is capable of leveraging body structural information from skeletons and strengthening the multilevel co-occurrence (MC) feature learning. Specifically, we first design the spatial attention module for feature enhancement of key joints from skeleton inputs. Second, we design MC memory units coupled with GCN to automatically model the spatial relationship between joints, and simultaneously capture the co-occurrence features from different joints, persons, and frames. Finally, we construct aggregated features of MCs (AFMCs) from MC memory units to better represent the intraframe action context encoding, and leverage a concurrent LSTM (Co-LSTM) to further model their temporal dynamics for action recognition. Our model significantly outperforms mainstream methods on NTU RGB+D 60/120 data set, mutual action subset of NTU RGB+D 60/120 data set, and Northewestern-UCLA data set.
Interactive e-book reading vs. paper-based reading: Comparing the effects of different mediums on middle school students’ reading comprehension

This exploratory study compared the effects of two different mediums of reading—interactive e-book reading and paper-based reading—on learners' reading comprehension. A specific focus was placed on literal vs. inferential comprehension. Thirty Korean middle school English language learners were randomly assigned to an interactive e-book reading (treatment) group or a paper-based reading (control) group. A pre-test and five comprehension tests were administered to both groups over a total of six reading sessions. A survey was also conducted to investigate students' perceptions of how helpful interactive e-book features were in aiding their reading comprehension. Results from the reading comprehension test scores showed no statistical differences between the groups across the five tests. Analysis of literal and inferential questions also showed no significant difference overall. Findings from the survey data along with researcher observation notes suggest that interactive features that are not designed to aid students' understanding can distract students from the task of reading which may hinder their comprehension. The overall results of this study suggest that it may not be the medium of reading, but how students engage with each medium that can affect their comprehension of text.
Multiscale Transfer Voting Mechanism: A New Strategy for Domain Adaption

Domain adaption models are widely applied to fault transfer diagnosis. However, the traditional domain adaption models can output only one high-dimensional transfer feature (TF); thus, it is difficult to capture domain-invariant information. Besides, using only one fully connected top classifier probably causes overfitting. Considering these two problems, in this article, we propose a multiscale transfer voting mechanism (MSTVM) to improve the classical domain adaption models and it can be universally applicable to any one of most domain adaption models. MSTVM consists of two substrategies: multiscale transfer mechanism (MSTM) and multiple transfer voting mechanisms (MTVM). The MSTM block includes several branches with multiscale convolutional and pooling operations, and it can output several multiscale TFs to strengthen the domain confusion. The MTVM block consists of multiple top classifiers and a plurality voting operation; thus, MTVM can effectively avoid overfitting and improve generalization ability. MSTVM has the advantages of MSTM and MTVM. Via two transfer diagnosis experiments, the advantage of MSTVM for improving various domain adaption models is verified.
Online sexist meme and its effects on moral and emotional processes in social media

Online sexist aggression is still overlooked in psychosocial literature. The present study aims to investigate moral cognitive and emotional processes associated with different online stances during a heated online discussion prompted by a sexist meme. To this end, adopting a social cognitive framework, we analyzed Twitter comments in response to the public condemnation of a rude sexist meme made about Carola Rackete, the captain of the Sea-Watch. A total of 1249 comments were codified for moral disengagement mechanisms, prosocial reasoning, and moral emotions. The results show the impact of a sexist meme in terms of cognitive and emotional processes. While moral disengagement mechanisms and other-condemning emotions have characterized aggressive stances, prosocial reasoning and other-suffering emotions have characterized prosocial ones. Intermediate stances also emerged during the online discussion, showing a more complex interplay between cognition and emotional moral processes—beyond the mere polarization of two stances. Indeed, some comments defending women image were characterized in active negative emotions (e.g., anger) and prosocial moral reasoning, whereas others "avoiding" comments were characterized by moral disengagement and ironic expressions. The social and theoretical implications of these results are then discussed.
Cryptocurrencies: A survey on acceptance, governance and market dynamics

Abstract This paper briefly overviews several challenging dimensions pertaining to cryptocurrencies with respect to their valuation, legitimacy, design, consensual acceptance and market‐based stylized facts with a view to understanding whether this new asset class indeed has the potential to become an alternative, or a replacement, to traditional fiat currencies. Our survey indicates that public embrace of cryptocurrencies continues to lag as the masses currently show reluctance in embracing cryptocurrencies as a complement, let alone a substitute to fiat counterparts. Governments have also successfully defended their sovereignty in preserving legal tender status, structural seigniorage and exclusivity. Market‐based studies hint at consistent inefficiencies across the spectrum. Furthermore, whether fundamental and mining factors determine cryptocurrencies' values remain unsettled. The most promising areas of research for crypto‐financial intelligentsia would be delving into establishing trial runs for central bank‐backed cryptocurrencies. In addition, we highlight that several methodological and data‐based obstacles remain in assessing the link between cryptocurrencies and their traditional rivals. This avenue remains a fertile ground for potential future research.
Parallel batch k-means for Big data clustering

The application of clustering algorithms is expanding due to the rapid growth of data volumes. Nevertheless, existing algorithms are not always effective because of high computational complexity. A new parallel batch clustering algorithm based on the k-means algorithm is proposed. The proposed algorithm splits a dataset into equal partitions and reduces the exponential growth of computations. The goal is to preserve the characteristics of the dataset while increasing the clustering speed. The centers of the clusters are calculated for each partition, which are merged and also clustered later. The approach to determine the optimal batch size is also considered. The statistical significance of the proposed approach is provided. Six experimental datasets are used to evaluate the effectiveness of the proposed parallel batch clustering. The obtained results are compared with the k-means algorithm. The analysis shows the practical applicability of the proposed algorithm to Big Data.
A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano

The research for developing an automatic recognition system of volcanic microearthquakes have been an important task around the world, based on this, the aim of this paper is to present an automatic recognition system of microearthquakes from the Cotopaxi Volcano based on a deep learning approach. The detection and classification stages were carried out with Convolutional Neural Networks by using spectrograms, which were generated according to the theory of periodograms with different types of windows. In order to enable the training of neural networks with a small database (1187 microearthquakes), the Transfer Learning process was used. This system operates in quasi-realtime, which is able to process records of 20min, accordingly to the requirements of the Instituto Geofísico de la Escuela Politécnica Nacional, with a recognition (detection + classification) time response of one minute, approximately. The system performance presents an accuracy of 99% in the detection stage and an accuracy of 97% in the classification stage.
Predicting software effort from use case points: A systematic review

Context: Predicting software project effort from Use Case Points (UCP) method is increasingly used among researchers and practitioners. However, unlike other effort estimation domains, this area of interest has not been systematically reviewed. Aims: There is a need for a systemic literature review to provide directions and supports for this research area of effort estimation. Specifically, the objective of this study is twofold: 1) to classify UCP effort estimation papers based on four criteria: contribution type, research approach, dataset type and techniques used with UCP; and 2) to analyze these paper from different views: estimation accuracy, favorable estimation context and impact of combined techniques on the accuracy of UCP. Method: We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, selecting quality papers, extracting data and drawing results. Result: The authors of UCP research paper, are generally not aware of previous published results and conclusions in the field of UCP effort estimation. There is a lack of UCP related publications in the top software engineering journals. This makes a conclusion that such papers are not useful for the community. Furthermore, most articles used small numbers of projects which cannot support generalizing the conclusion in most cases. Conclusions: There are multiple research directions for UCP method that have not been examined so far such as validating the algebraic construction of UCP based on industrial data. Also, there is a need for standard automated tools that govern the process of translating use case diagram into its corresponding UCP metrics. Although there is an increase interest among researchers to collect industrial data and build effort prediction models based on machine learning methods, the quality of data is still subject to debate.
An Integrated CNN-LSTM Model for Bangla Lexical Sign Language Recognition

The development of Human-Computer Interaction (HCI) will not only empower the disabled with access to technology but also help to build tools for assisting the disable and their caregivers. Some disabled people are deaf, they use signs to communicate. Signs are emblematic forms which convey a specific meaning. These emblems construct the sign language, which is a non-verbal language used by the deaf community. This research attempts to build a Bangla Sign language recognition system that can recognize signs of both hands. Hands vary in shapes and sizes, as well as signs vary in orientations and motions. Accurate feature extraction is necessary for such systems. For such purposes, deep learning approaches can prove to be effective for the classification and feature engineering of images. In the beginning, an integrated CNN-LSTM model is proposed for building a Sign language Recognition System, a Bangla Sign Language (BSL) dataset consisting of Bangla lexical signs is considered. This dataset consists of 13,400 images comprising thirty-six classes of Bangla lexical signs. The model is trained using a CNN-LSTM model. This model produces a training accuracy of 90% and a testing accuracy of 88.5%. The proposed model is compared to CNN and other CNN state of the art models, namely VGG16, VGG9 and MobileNet. The CNN model and other CNN state undergo the problem of overfitting as their training accuracy is greater than the testing accuracy.
Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone

Activity recognition can provide useful information about an older individual's activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphone accelerometry data of older people. Deep learning algorithms, including convolutional neural network (CNN) and long short-term memory (LSTM), were evaluated in this study. Smartphone accelerometry data of free-living activities, performed by 53 older people (83.8 ± 3.8 years; 38 male) under standardized circumstances, were classified into lying, sitting, standing, transition, walking, walking upstairs, and walking downstairs. A 1D CNN, a multichannel CNN, a CNN-LSTM, and a multichannel CNN-LSTM model were tested. The models were compared on accuracy and computational efficiency. Results show that the multichannel CNN-LSTM model achieved the best classification results, with an 81.1% accuracy and an acceptable model and time complexity. Specifically, the accuracy was 67.0% for lying, 70.7% for sitting, 88.4% for standing, 78.2% for transitions, 88.7% for walking, 65.7% for walking downstairs, and 68.7% for walking upstairs. The findings indicated that the multichannel CNN-LSTM model was feasible for smartphone-based activity recognition in older people.
Automatic Human Emotion Recognition System using Facial Expressions with Convolution Neural Network

Emotion recognition using facial expression is very much necessary these days. Different kinds of emotions reflect a different definitions. Facial emotion recognition plays a major role in driver warning systems, it can also play an important role in shopping malls to predict unusual activity like terrorist attacks, robbery and much more. Predicting the suicidal tendency of a person also can be done using facial emotion recognition. An automatic facial emotion classification system is proposed in this paper using the Convolution Neural Network (CNN) with the features extracted from the Speeded Up Robust Features (SURF). 91% accuracy is achieved with the proposed model which supports tracking human emotion with facial expressions.
Detecting Meaningful Clusters From High-Dimensional Data: A Strongly Consistent Sparse Center-Based Clustering Approach

In context to high-dimensional clustering, the concept of feature weighting has gained considerable importance over the years to capture the relative degrees of importance of different features in revealing the cluster structure of the dataset. However, the popular techniques in this area either fail to perform feature selection or do not preserve the simplicity of Lloyd's heuristic to solve the k-means problem and the like. In this paper, we propose a Lasso Weighted k-means ( LW- k-means) algorithm, as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ( p) can be much higher than the number of observations ( n). The LW- k-means method imposes an l1 regularization term involving the feature weights directly to induce feature selection in a sparse clustering framework. We develop a simple block-coordinate descent type algorithm with time-complexity resembling that of Lloyd's method, to optimize the proposed objective. In addition, we establish the strong consistency of the LW- k-means procedure. Such an analysis of the large sample properties is not available for the conventional sparse k-means algorithms, in general. LW- k-means is tested on a number of synthetic and real-life datasets and through a detailed experimental analysis, we find that the performance of the method is highly competitive against the baselines as well as the state-of-the-art procedures for center-based high-dimensional clustering, not only in terms of clustering accuracy but also with respect to computational time.
Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine

Through evaluating the weight of evidence method and calculating the information value (IV), this article proposes a method to evaluate the credit qualities of borrowers based on the extreme learning machine, the fuzzy c-means (FCM) algorithm, and the calculation of a confusion matrix. Through screening credit rating indexes, we established a credit scoring model of the borrower. In addition, we constructed formulas to determine the probability of default and default loss rate. The model also classifies the credit qualities of borrowers. In addition, we designed a selection algorithm for the borrower's credit quality rating index, and a borrower's credit quality rating algorithm. This paper collects sample data of 7706 borrowers of Renren loans from the Internet. The credit scores of the borrower, the default probability, and the default loss rate of each type of borrower are calculated, and the repayment status of borrowers are analyzed. We divided the borrowers into 7 grades and 5 grades by calculating a confusion matrix. The experimental results show that the overall accuracy of the credit scoring model is 98.5%, in which the accuracy for non-default samples is 98.9%, and the accuracy for default samples is 88.3%. The accuracy of the established credit quality rating model proved to be relatively high, and it can provide important reference values and scientific guidance for banks, financial institutions, and major financial platforms. It can also judge and predict default behavior.
AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 (C5) is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features.
Polymorphic Adversarial DDoS attack on IDS using GAN

Intrusion Detection systems are important tools in preventing malicious traffic from penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly enhancing their detection capabilities using machine learning algorithms. However, these algorithms are vulnerable to new unknown types of attacks that can evade machine learning IDS. In particular, they may be vulnerable to attacks based on Generative Adversarial Networks (GAN). GANs have been widely used in domains such as image processing, natural language processing to generate adversarial data of different types such as graphics, videos, texts, etc. We propose a model using GAN to generate adversarial DDoS attacks that can change the attack profile and can be undetected. Our simulation results indicate that by continuous changing of attack profile, defensive systems that use incremental learning will still be vulnerable to new attacks.
Multi-Drone-Based Single Object Tracking With Agent Sharing Network

Drones equipped with cameras (UAVs) can dynamically track the target in the air from a broader view compared with static cameras or moving sensors over the ground. However, it is still challenging to accurately track the target using a single drone due to several factors such as appearance variations and severe occlusions. To this end, we collect a new <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> rone single <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> bject <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> racking ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDOT</b> ) dataset that consists of 92 groups of video clips with 113, 918 high resolution frames taken by two drones and 63 groups of video clips with 145, 875 high resolution frames taken by three drones. Besides, two evaluation metrics are specially designed for multi-drone single object tracking, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , automatic fusion score (AFS) and ideal fusion score (IFS). Moreover, the agent sharing network (ASNet) is proposed by integrating self-supervised template sharing, target re-detection, and view-aware fusion of the target from multiple drones into a unified framework, which can improve the tracking accuracy significantly compared with single drone tracking. Extensive experiments on MDOT show that our ASNet significantly outperforms recent state-of-the-art trackers. The dataset can be found in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/VisDrone/MultiDrone</uri> .