Reviewerly/exHarmony huggingface dataset:
A novel feature descriptor based coral image classification using extreme learning machine with ameliorated chimp optimization algorithm

Coral reefs are one of the most biodiverse and productive ecosystems on earth, which acts as a habitat and provides food to a wide range of organisms. The coral reefs protect the coasts from storms, waves, and erosion. Unfortunately, this ecosystem is under threat by various natural and manmade factors, including pollution, ocean acidification, sedimentation, and climate change. The marine ecosystems need to be monitored regularly in order to minimize the aforementioned threats. Hence, an automated classification system is necessary for the classification of coral images. In this paper, a novel method called “Systematic Local Directional Encoded Derivative Binary Pattern” (SLDEDBP) is proposed to extract the coral reef feature values. In this descriptor, the feature values are derived using two phases: Local Directional Derivative Binary Pattern (LDDBP) and Systematic Local Discriminate Binary pattern (SLDBP). In the LDDBP phase, the eight directional kirsch mask is employed in each 3 × 3 neighbourhood window to extract the directional edge response of an image. Then, in the SLDBP phase, the local neighbourhood pixel values are divided into four non-overlapping groups of Z, M, TZ (Tilted Z), and TM (Tilted M) to extract unique features values by considering the centre pixel value in a 3 × 3 neighbourhood window. Finally, the derived feature values of LDDBP and SLDBP are concatenated to procedure final discriminate feature values. The extracted feature values of coral images are applied to a hybridized method called Ameliorated Chimp Optimization Algorithm with Extreme Learning Machine Classifier (AChOA-ELM) to optimize the relative weight of the input and biases of neural networks. This avoids overfitting and makes the model more generalized and robust. The proposed AChOA-ELM method performance is evaluated using different databases of coral and standard images. The reliability of the proposed model has been evaluated utilizing the model performance metrics, accuracy (ACC), true positive rate (TPR), true negative rate (TNR), false-positive rate (FPR), precision (PRC), positive predictive value (PPV), and F1 score. The findings reveal that the proposed hybrid AChOA-ELM achieves greater classification accuracy with a faster coverage rate than other existing methods. In the presence of the image data augmentation technique, the EILAT2 dataset achieves the highest classification accuracy of 99%, whereas MLC dataset achieves the lowest classification accuracy of 94.86%.
Technology penetration and human development nexus in middle-income countries: the synergy effect of inclusive resources distribution

This paper intends to examine how interactions between equal distribution of resources and the information and communication technology (ICT) influence inclusive human development (inequality-adjusted human development) for 81 countries from middle-income countries within the period 2005–2017. We use a double-censored Tobit regression as it accounts for the dependent variable with a limited range. It exhibits the behavior that is consistent with the method of estimation. We employ the instrumental variable (IV) for the independent variables of interest to deal with simultaneity or reverse causality due to endogeneity. In light of established findings for this study, we conclude that equal distribution of public goods such as technologies could play a critical role in promoting inclusive human development. Supplementary policy repercussions are highlighted.
A new method of vibration signal denoising based on improved wavelet

Noise cancellation is one of the most successful applications of the wavelet transform. Its basic idea is to compare wavelet decomposition coefficients with the given thresholds and only keep those bigger ones and set those smaller ones to zero and then do wavelet reconstruction with those new coefficients. It is most likely for this method to treat some useful weak components as noise and eliminate them. Based on the cyclostationary property of vibration signals of rotating machines, a novel wavelet noise cancellation method is proposed. A numerical signal and an experimental signal of rubbing fault are used to test and compare the performances of the new method and the conventional wavelet based denoising method provided by MATLAB. The results show that the new noise cancellation method can efficaciously suppress the noise component at all frequency bands and has better denoising performance than the conventional one.
Classification of cervical cells leveraging simultaneous super-resolution and ordinal regression

Automatic classification of cervical cells plays a critical role in the Computer-assisted Cytology Test (CCT) system. The efficiency of the CCT system can be promoted by sacrificing the microscopic image resolution to speed up the microscopic image acquisition. In this case, the low resolution of the cell image will severely deteriorate the performance of available Convolutional Neural Networks (CNN) based classification methods. Inspired by the positive effect of super-resolution in addressing classification or recognition tasks, we propose a cervical cell classification algorithm leveraging simultaneous super-resolution, which is achieved using Generative Adversarial Network (GAN) techniques. Our framework is designed in an end-to-end manner wherein the classification loss is back-propagated into the super-resolution network during training. Moreover, we perform ordinal regression with smooth L1 loss to further improve the classification results. Extensive experiments have verified the effectiveness of our method. Our simultaneous super-resolution based method achieves 93.5% classification accuracy on the 6-class Heer dataset, outperforming the method using only the state-of-the-art classifier by an obvious margin of 3.2%. Besides, our ordinal regression method significantly improves the MAE (Mean Absolute Error) by 0.0143 and 1-off accuracy by 0.95% on the 4-class Heer dataset. For the Herlev dataset, our method yields the classification accuracy of 98.1% and 97.6% for the 2-class and 7-class problems, which is still competitive even with low-resolution input.
Eye Movement Patterns Reflecting Cybersickness: Evidence from Different Experience Modes of a Virtual Reality Game

This study investigated whether the degree of cybersickness varies depending on different virtual reality experience modes (playing vs. watching) and whether specific eye movement parameters reflect changes in cybersickness. Simulator Sickness Questionnaire results from 20 participants (10 playing and 10 watching) showed that cybersickness was much more severe in the watching mode, particularly during the second of the three total trials. Moreover, cybersickness' changing pattern was reflected in the center gaze ratio and scan-path length. These findings imply the importance of physiological measurements for a deeper understanding of cybersickness in theoretical and practical respects.
Comparing the impact of Internet of Things and cloud computing on organisational behavior: A survey

Cloud computing is about delivery of different computing services involving databases, analytics, software, networking with the use of internet to enhance innovation, incorporate flexibility in resources and broaden profitability. However, Internet of Things (IoT) is an essential system for interrelating computer devices, digital machines, people and others which are offered with unique identifiers where data can be transferred with human involvement and wireless network. 42% of organisations in UK use cloud computing. The problem with cloud computing revolves around security and privacy issues as data is stored by a third party from inside or outside of the organisation leading to broken authentication, compromising of credentials and others. The use of IoT is vulnerable as it provides connectivity to devices, machines and people therefore, it needs to contain more storage that is made from cloud facilities. Survey has been conducted where primary quantitative method has been considered to obtain data from 101 managers of the organisation that has adopted cloud computing and IoT. However, 8 close-ended questions have been asked to 101 managers. Positivism philosophy has been used to make quantifiable observations along with descriptive design and others. The results and discussion will analyse responses of the respondents after conducting statistical analysis. However, research has been revolving around making a comparison between using cloud computing and IoT along with analysing organisational behaviour.
MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection

Hybrid spam is an undesirable e-mail (electronic mail) that contains both image and text parts. It is more harmful and complex as compared to image-based and text-based spam e-mail. Thus, an efficient and intelligent approach is required to distinguish between spam and ham. To our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. Most of these multimodal architectures adopted the decision-level fusion method, whereby the classification scores of each modality were concatenated and fed to another classification model to make a final decision. Unfortunately, this method not only demands many learning steps, but it also loses correlation in mixed feature space. In this paper, we propose a deep multimodal feature-level fusion architecture that concatenates two embedding vectors to have a strong representation of e-mails and increase the performance of the classification. The paragraph vector distributed bag of words (PV-DBOW) and the convolutional neural network (CNN) were used as feature extraction techniques for text and image parts, respectively, of the same e-mail. The extracted feature vectors were concatenated and fed to the random forest (RF) model to classify a hybrid e-mail as either spam or ham. The experiments were conducted on three hybrid datasets made using three publicly available corpora: Enron, Dredze, and TREC 2007. According to the obtained results, the proposed model provides a higher accuracy of 99.16% compared to recent state-of-the-art methods.
Fuzzy clustering of Acute Lymphoblastic Leukemia images assisted by Eagle strategy and morphological reconstruction

Patients with Acute Lymphoblastic Leukemia (ALL) require prompt diagnosis since it has the chance to become fatal if neglected for a few weeks. The microscopic image of lymphocyte cells creates doubts even for expert pathologists because normal lymphocytes cells and ALL blast cells are both very smooth. Therefore, proper segmentation of White Blood Cells (WBC) is a very crucial aspect of the task. Consequently, the focus of the study is on segmenting the WBCs in Acute Lymphoblastic Leukemia (ALL) images utilizing classical crisp and fuzzy clustering approaches like K-means (KM) and Fuzzy C-means (FCM). But these classical clustering approaches are very sensitive to noise and initial cluster center initialization and hence trapped in local optima. As a result, these techniques may produce incorrect cluster centers. Firstly, researchers are employing Nature-Inspired Optimization Algorithms (NIOAs) as an alternate methodology for both crisp and fuzzy clustering problems to solve the initial cluster center initialization issue. Therefore, using a two-stage Eagle Strategy based on Stochastic Fractal Search (SFS) method, this research proposes a fuzzy clustering methodology. Secondly, morphological reconstruction has been employed for filtering the membership matrix to guarantee noise-immunity. A scrupulous parallel study is performed among the proposed eagle strategy based fuzzy clustering depending on morphological reconstruction with some well-known NIOA based fuzzy clustering and crisp clustering approaches, and classical clustering methodologies like KM and FCM in view of a collection of color ALL images and regular performance metrics. Experimental results demonstrate that recommended ES-SFS based fuzzy clustering technique with morphological reconstruction surpasses the most of utilized approaches in words of computation effort, quality metrics, and robustness. Additionally, to get rid of the random effect in the achieved numerical results, a non-parametric strategy is used for statistical validation.
Unsupervised random forest for affinity estimation

Abstract This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node. The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.
Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge

Small-scale data centers at the edge are becoming prominent in offering various services to the end-users following the cloud model while avoiding the high latency inherent to the classic cloud environments when accessed from remote Internet regions. However, we should address several challenges to facilitate the end-users finding and consuming the relevant services from the edge at the Internet scale. First, the scale and diversity of the edge hinder seamless access. Second, a framework where researchers openly share their services and data in a secured manner among themselves and with external consumers over the Internet does not exist. Third, the lack of a unified interface and trust across the service providers hinder their interchangeability in composing workflows by chaining the services. Thus, creating a workflow from the services deployed on the various edge nodes is presently impractical. This paper designs Viseu, a latency-aware blockchain framework to provide Virtual Internet Services at the Edge. Viseu aims to solve the puzzle of network service discovery at the edge, considering the peers' reputation and latency when choosing the service instances. Viseu enables peers to share their computational resources, services, and data among each other in an untrusted environment, rather than relying on a set of trusted service providers. By composing workflows from the peers' services, rather than confining them to the pre-established service provider and consumer roles, Viseu aims to facilitate scientific collaboration across the peers natively. Furthermore, by offering services from multiple peers close to the end-users, Viseu also minimizes end-to-end latency and data loss in the service execution at the Internet scale.
An Empirical Study to Detect Cyberbullying with TF-IDF and Machine Learning Algorithms

The headway of technology has enabled various aptitudes like the internet and social media platforms evolving within the internet. Apart from the offerings, there is also the dark side named cyberbullying which is defined as the abusive and personal attacking regarding social interactions on various social platforms. The aftermath of this heinous job is harrowing and tormentous. One momentary solution can evolve the detection of texts regarding cyberbullying. TF-IDF has shown preeminent textual feature map calculation over the years to solve the ambiguity of cross-domain words. Moreover, supervised machine learning algorithms allude to wondrous performance in classifying textual data. Bearing that in mind, a combination of machine learning algorithms namely SVM (Support Vector Machine), Logistic Regression, Decision Tree, Random Forest and MNB (Multinomial Naïve Bayes) has been advocated with TF-IDF features to classify cyberbullying from text data. Two different approaches have been engendered namely the baseline approach that proceeds with imbalanced data and SMOTE (Synthetic Minority Over-sampling Technique) approach for balancing the data where Random Forest with SMOTE approach has achieved an accuracy of 89% with an f1-score of 89% in this empirical study.
Adaptive personalized recommender system using learning automata and items clustering

The personalized recommender systems provide user-related services based on user preferences; these preferences are recorded in an individual profile. Therefore, the more complete and precise each user profile leads more successful the recommendation process. The people’s interests change over time though traditional researches do not follow these changes regularly. Under such circumstances, designing an efficient user model to track users’ interests is greatly important. In the current study, we suggest an algorithm to create the learning automata-based user profiling. Due to many items and the commonality of features between them, we clustered items. In this technique, a learning automaton is assigned to the active user. The learning automaton adjusts the amount of user interest in each cluster based on user feedback. As the user interactions with the system increase, the internal state of the learning automaton converges towards the user’s genuine interests in the item clusters. The experimental results demonstrate that our algorithm outperforms compared approaches in precision, recall, RMSE, and MAE. In addition, the proposed algorithm for new users has acceptable performance.
KubeHICE: Performance-aware Container Orchestration on Heterogeneous-ISA Architectures in Cloud-Edge Platforms

To manage edge applications more conveniently, cloud providers extend the container and the container orchestrator from the cloud to the edge. The existing container orchestrators are designed for homogenous clusters at the cloud, while for nodes in cloud-edge computing environments, the hardware is commonly heterogeneous. Thus, current container orchestrators cannot automatically deal with heterogeneous-ISA. Besides, even if the nodes in a cloud-edge platform have the same ISA, their performance may be significantly different. The main reason lies in that the resource model of current container orchestrators is designed for homogenous clusters and assumes that all CPU cores of every node have the same performance, which will lead to low resource utilization and overload as far as heterogeneous clusters are concerned. In this paper, we present the design and implementation of KubeHICE, a performance-aware container orchestrator for heterogeneous-ISA architectures in cloud-edge platforms, which extends open source Kubernetes by AIM (Automatic Instruction Set Architecture Matching) and PAS (Performance-Aware Scheduling), two novel functional approaches that we propose. AIM is proposed for handling heterogeneous ISA, it can automatically find a node that is compatible with ISAs supported by the containerized application. PAS is designed for scheduling containers according to computing capability of cluster nodes. For PAS, we build a CPU resource allocation model to describe the computing capability differences of CPUs in a heterogeneous-ISA cluster. Based on this model, PAS is capable of estimating single-thread performance of a CPU through the workloads, which therefore enables KubeHICE to estimate the performance differences between containers that are assigned to heterogeneous nodes. Experimental results show that KubeHICE adds no additional overhead to container orchestration and it is effective in performance estimation and resource scheduling. We demonstrate the advantages of KubeHICE in several real-world scenarios, proving for example up to 40% increase in CPU utilization when eliminating heterogeneity. We believe that KubeHICE is not only technically beneficial to containerized applications in heterogeneous cloud-edge platforms, but also can a referable solution for Kubernetes community to support future IoT or edge server enabled container orchestration.
A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.