Reviewerly/exHarmony huggingface dataset:
Artificial intelligence and online extremism

Radicalisation is a process that historically used to be triggered mainly through social interactions in places of worship, religious schools, prisons, meeting venues, etc. Today, this process is often initiated on the Internet, where radicalisation content is easily shared, and potential candidates are reached more easily, rapidly, and at an unprecedented scale (Edwards and Gribbon, 2013; Von Behr et al., 2013).In recent years, some terrorist organisations succeeded in leveraging the power of social media to recruit individuals to their cause and ideology (Farwell, 2014). It is often the case that such recruitment attempts are initiated on open social media platforms (e.g., Twitter, Facebook, Tumblr, YouTube) but then move onto private messages and/or encrypted platforms (e.g., WhatsApp, Telegram). Such encrypted communication channels have also been used by terrorist cells and networks to plan their operations (Gartenstein-Ross and Barr).To counteract the activities of such organisations, and to halt the spread of radicalisation content, some governments, social media platforms, and counter-extremism agencies are investing in the creation of advanced information technologies to identify and counter extremism through the development of Artificial Intelligent (AI) solutions (Correa and Sureka, 2013; Agarwal and Sureka 2015a; Scrivens and Davies, 2018).These solutions have three main objectives: (i) understanding the phenomena behind online extremism (the communication flow, the use of propaganda, the different stages of the radicalisation process, the variety of radicalisation channels, etc.), (ii) automatically detecting radical users and content, and (iii) predicting the adoption and spreading of extremist ideas.Despite current advancements in the area, multiple challenges still exist, including: (i) the lack of a common definition of prohibited radical and extremist internet activity, (ii) the lack of solid verification of the datasets collected to develop detection and prediction models, (iii) the lack of cooperation across research fields, since most of the developed technological solutions are neither based on, nor do they take advantage of, existing social theories and studies of radicalisation, (iv) the constant evolution of behaviours associated with online extremism in order to avoid being detected by the developed algorithms (changes in terminology, creation of new accounts, etc.) and, (v) the development of ethical guidelines and legislation to regulate the design and development of AI technology to counter radicalisation.In this book chapter we provide an overview of the current technological advancements towards addressing the problem of online extremism (with a particular focus on Jihadism). We identify some of the limitations of current technologies, and highlight some of the potential opportunities. Our aim is to reflect on the current state of the art and to stimulate discussions on the future design and development of AI technology to target the problem of online extremism.
Estimation of Wind Turbine Angular Velocity Remotely Found on Video Mining and Convolutional Neural Network

Today, energy issues are more important than ever. Because of the importance of environmental concerns, clean and renewable energies such as wind power have been most welcomed globally, especially in developing countries. Worldwide development of these technologies leads to the use of intelligent systems for monitoring and maintenance purposes. Besides, deep learning as a new area of machine learning is sharply developing. Its strong performance in computer vision problems has conducted us to provide a high accuracy intelligent machine vision system based on deep learning to estimate the wind turbine angular velocity, remotely. This velocity along with other information such as pitch angle and yaw angle can be used to estimate the wind farm energy production. For this purpose, we have used SSD (Single Shot Multi-Box Detector) object detection algorithm and some specific classification methods based on DenseNet, SqueezeNet, ResNet50, and InceptionV3 models. The results indicate that the proposed system can estimate rotational speed with about 99.05 % accuracy.
Detecting Perceived Emotions in Hurricane Disasters

Natural disasters (e.g., hurricanes) affect millions of people each year, causing widespread destruction in their wake. People have recently taken to social media websites (e.g., Twitter) to share their sentiments and feelings with the larger community. Consequently, these platforms have become instrumental in understanding and perceiving emotions at scale. In this paper, we introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups. Our best BERT model, even after task-guided pre-training which leverages unlabeled Twitter data, achieves only 68% accuracy (averaged across all groups). HurricaneEmo serves not only as a challenging benchmark for models but also as a valuable resource for analyzing emotions in disaster-centric domains.
An Approach Focusing on the Convolutional Layer Characteristics of the VGG Network for Vehicle Tracking

Object tracking is a key technology in the field of intelligent transportation. To solve the partial occlusion problem in vehicle tracking, this paper analyzes the characteristics of a VGG convolutional neural network by experimental observation and describes its characteristics: (1) feature maps can be used for positioning, but they have redundancies, and (2) different layers of feature maps have different characteristics. After these characteristics are applied to vehicle tracking, a vehicle tracking algorithm is designed. For a given target vehicle, feature maps are generated on convolutional layers conv4_4 and conv5_4 of the VGG network, and the feature maps most relevant to the target vehicle are selected. These feature maps are used to capture target vehicle information and distinguish the target vehicle from backgrounds with similar appearances. The experiments use vehicle data from the LaSOT, VOT2017 and OTB2015 datasets to compare the vehicle tracking results of our proposed algorithm with those of other algorithms. The results show that the method proposed in this paper has certain advantages. According to algorithm implementation and vehicle tracking experiments, the proposed vehicle tracking method can solve the drift problem and is better than the traditional method at addressing drift problems.
Intelligent Association Classification Technique for Phishing Website Detection

Many critical applications need more accuracy and speed in the decision making process. Data mining scholars developed set of artificial automated tools to enhance the entire decisions based on type of application. Phishing is one of the most critical application needs for high accuracy and speed in decision making when a malicious webpage impersonates as legitimate webpage to acquire secret information from the user. In this paper, we proposed a new Association Classification (AC) algorithm as an artificial automated tool to increase the accuracy level of the classification process that aims to discover any malicious webpage. An Intelligent Association Classification (IAC) algorithm developed in this article by employing the Harmonic Mean measure instead of the support and confidence measure to solve the estimation problem in these measures and discovering hidden pattern not generated by the existing AC algorithms. Our algorithm compared with four well-known AC algorithm in terms of accuracy, F1, Precision, Recall and execution time. The experiments and the visualization process show that the IAC algorithm outperformed the others in all cases and emphasize on the importance of the general and specific rules in the classification process
Using PRMSE to evaluate automated scoring systems in the presence of label noise

The effect of noisy labels on the performance of NLP systems has been studied extensively for system training.In this paper, we focus on the effect that noisy labels have on system evaluation.Using automated scoring as an example, we demonstrate that the quality of human ratings used for system evaluation have a substantial impact on traditional performance metrics, making it impossible to compare system evaluations on labels with different quality.We propose that a new metric, proportional reduction in mean squared error (PRMSE), developed within the educational measurement community, can help address this issue, and provide practical guidelines on using PRMSE.
Ensemble Fuzzy Feature Selection Based on Relevancy, Redundancy, and Dependency Criteria

The main challenge of classification systems is the processing of undesirable data. Filter-based feature selection is an effective solution to improve the performance of classification systems by selecting the significant features and discarding the undesirable ones. The success of this solution depends on the extracted information from data characteristics. For this reason, many research theories have been introduced to extract different feature relations. Unfortunately, traditional feature selection methods estimate the feature significance based on either individually or dependency discriminative ability. This paper introduces a new ensemble feature selection, called fuzzy feature selection based on relevancy, redundancy, and dependency (FFS-RRD). The proposed method considers both individually and dependency discriminative ability to extract all possible feature relations. To evaluate the proposed method, experimental comparisons are conducted with eight state-of-the-art and conventional feature selection methods. Based on 13 benchmark datasets, the experimental results over four well-known classifiers show the outperformance of our proposed method in terms of classification performance and stability.
Transfer of angular spectrum in parametric down-conversion with structured light

We develop the formal approach to the angular spectrum transfer in parametric down-conversion that allows pumping with a structured beam. The scheme is based on an entangled photon source pumped by a laser beam structured with a vector-vortex polarization profile. This creates a two-photon quantum state exhibiting polarization-dependent transverse correlations that can be accessed through coincidence measurements on the spatially separated photons. The calculated correlations are shown to present a spin-orbit profile typical of vector beams, however, distributed on separate measurement regions. Our approach allows the design of the pump beam vector spatial structure and measurement strategies for potential applications of these entangled states, such as in quantum communication.
Stance Prediction for Contemporary Issues: Data and Experiments

We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which input phrases contribute to stance detection.
Study on reasonable initialization enhanced Karnik-Mendel algorithms for centroid type-reduction of interval type-2 fuzzy logic systems

Type-reduction (TR) is a key block for interval type-2 fuzzy logic systems (IT2 FLSs). In general, Karnik-Mendel (KM) (or enhanced Karnik-Mendel (EKM)) algorithms are used to perform the TR. These two types of algorithms have the advantage of preserving the uncertainties of membership functions (MFs) flow in IT2 FLSs. This paper gives the initialization explanations of KM and EKM algorithms, and proposes reasonable initialization enhanced Karnik-Mendel (RIEKM) algorithms for centroid TR of IT2 FLSs. By considering the accurate continuous Nie-Tan (CNT) algorithms as the benchmark, four computer simulation examples are adopted to illustrate and analyze the performances of RIEKM algorithms for solving the centroid TR and defuzzification of IT2 FLSs. Compared with the EKM algorithms, the proposed RIEKM algorithms have smaller absolute errors and faster convergence speeds, which afford the potential value for designing and applying IT2 FLSs.
SHREC 2020: Retrieval of digital surfaces with similar geometric reliefs

This paper presents the methods that have participated in the SHREC'20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local "geometric pattern" information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches.
Impact of School Enrolment Size on Student’s Achievements

Education is a basic need of humanity and key element for the development of a nation. The population is increasing day by day and the main problem of education particularly in primary education is the increasing of number of students in the schools. So, the enrolment size of schools is increasing consistently. The effect of school enrolment size on the student’s achievement has been a major factor for the failure of the education system especially primary education. The purpose of this research is to discover the possible relations between the school enrolment size and student’s achievements. The data of four primary schools namely GPS Gaggu, GPS Samina, GPS Tagiani and GPS Yaroo has been selected to check the effect of school enrolment size on the student’s achievements. The results of the study indicate that as the enrolment size increased in the school, the achievements of the student’s decreased and vice versa.
Interferometric approach to open quantum systems and non-Markovian dynamics

We combine the dynamics of open quantum systems with interferometry and interference, introducing the concept of an open system interferometer. By considering a single photon in a Mach-Zehnder interferometer, where the polarization (open system) and frequency (environment) of the photon interact, we theoretically show that inside the interferometer, pathwise polarization dephasing dynamics is Markovian while the joint dynamics displays non-Markovian features. Outside the interferometer and due to interference, the open system displays rich dynamical features with distinct alternatives: only one path displays non-Markovian memory effects, both paths individually display them, or no memory effects appear at all. The scheme allows one to (1) probe the optical path difference inside the interferometer by studying pathwise non-Markovianity outside the interferometer, and (2) introduce pathwise dissipative features for the open system dynamics even though the system-environment interaction itself contains only dephasing. Due to the path dependencies, our results are tightly connected to quantum erasure. In general, our results open up alternative ways to control open system dynamics and for fundamental studies of quantum physics.
Intelligent manufacturing security model based on improved blockchain

The Industrial Internet of Things (IIoT) plays an important role in the development of smart factories. However, the existing IIoT systems are prone to suffering from single points of failure and unable to provide stable service. Meanwhile, with the increase of node scale and network quantity, the maintenance cost presents to be higher. Such a disadvantage can be effectively compensated by the features such as security, privacy, non-tamperability and distributed deployment supported by the blockchain. In this paper, first, an intelligent manufacturing security model based on blockchain was proposed. Due to the high power consumption and low throughput of the traditional blockchain, IoT devices with limited power consumption can not work independently. Therefore, in this paper, a new Merkle Patricia tree (MPT) was adopted to extend the blockchain structure and provide fast query of node status. Second, since the MPT does not support concurrent operation and the data operation performance deteriorates with high data volume, a lock-free concurrent and cache-based Merkle Patricia tree was proposed (CMPT) to support lock-free concurrent data operation, which can improve the data operation efficiency in multi-core system. The experimental results indicate that, compared with the original MPT, the CMPT proposed in this paper effectively reduced the time complexity of data insertion and data query and improved the speed of block construction and data query.
Point-Sampling Method Based on 3D U-Net Architecture to Reduce the Influence of False Positive and Solve Boundary Blur Problem in 3D CT Image Segmentation

Malignant lesions are a huge threat to human health and have a high mortality rate. Locating the contour of organs is a preparation step, and it helps doctors diagnose correctly. Therefore, there is an urgent clinical need for a segmentation model specifically designed for medical imaging. However, most current medical image segmentation models directly migrate from natural image segmentation models, thus ignoring some characteristic features for medical images, such as false positive phenomena and the blurred boundary problem in 3D volume data. The research on organ segmentation models for medical images is still challenging and demanding. As a consequence, we redesign a 3D convolutional neural network (CNN) based on 3D U-Net and adopted the render method from computer graphics for 3D medical images segmentation, named Render 3D U-Net. This network adapts a subdivision-based point-sampling method to replace the original upsampling method for rendering high-quality boundaries. Besides, Render 3D U-Net integrates the point-sampling method into 3D ANU-Net architecture under deep supervision. Meanwhile, to reduce false positive phenomena in clinical diagnosis and to achieve more accurate segmentation, Render 3D U-Net specially designs a module for screening false positive. Finally, three public challenge datasets (MICCAI 2017 LiTS, MICCAI 2019 KiTS, and ISBI 2019 segTHOR) were selected as experiment datasets and to evaluate the performance on target organs. Compared with other models, Render 3D U-Net improved the performance on both overall organ and boundary in the CT image segmentation tasks, including in the liver, kidney, and heart.
Asymmetrical Multi-task Attention U-Net for the Segmentation of Prostate Bed in CT Image

Segmentation of the prostate bed, the residual tissue after the removal of the prostate gland, is an essential prerequisite for post-prostatectomy radiotherapy but also a challenging task due to its non-contrast boundaries and highly variable shapes relying on neighboring organs. In this work, we propose a novel deep learning-based method to automatically segment this “invisible target”. As the main idea of our design, we expect to get reference from the surrounding normal structures (bladder&rectum) and take advantage of this information to facilitate the prostate bed segmentation. To achieve this goal, we first use a U-Net as the backbone network to perform the bladder&rectum segmentation, which serves as a low-level task that can provide references to the high-level task of the prostate bed segmentation. Based on the backbone network, we build a novel attention network with a series of cascaded attention modules to further extract discriminative features for the high-level prostate bed segmentation task. Since the attention network has one-sided dependency on the backbone network, simulating the clinical workflow to use normal structures to guide the segmentation of radiotherapy target, we name the final composition model asymmetrical multi-task attention U-Net. Extensive experiments on a clinical dataset consisting of 186 CT images demonstrate the effectiveness of this new design and the superior performance of the model in comparison to the conventional atlas-based methods for prostate bed segmentation. The source code is publicly available at https://github.com/superxuang/amta-net.
A close‐up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation

The misclassification error distance and the adjusted Rand index are two of the most common criteria used to evaluate the performance of clustering algorithms. This paper provides an in-depth comparison of the two criteria, with the aim of better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data analysis examples and the study of particular cases in great detail. An exhaustive simulation study provides insight into the criteria distributions and reveals some previous misconceptions.
GigaBERT: Zero-shot Transfer Learning from English to Arabic

Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer. However, their performance on Arabic information extraction (IE) tasks is not very well studied. In this paper, we pre-train a customized bilingual BERT, dubbed GigaBERT, that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. We study GigaBERT's effectiveness on zero-short transfer across four IE tasks: named entity recognition, part-of-speech tagging, argument role labeling, and relation extraction. Our best model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT (Antoun et al., 2020) in both the supervised and zero-shot transfer settings. We have made our pre-trained models publicly available at this https URL.
Bent and $${{\mathbb {Z}}}_{2^k}$$-Bent functions from spread-like partitions

Bent functions from a vector space $${{\mathbb {V}}}_n$$ over $${{\mathbb {F}}}_2$$ of even dimension $$n=2m$$ into the cyclic group $${{\mathbb {Z}}}_{2^k}$$ , or equivalently, relative difference sets in $${{\mathbb {V}}}_n\times {{\mathbb {Z}}}_{2^k}$$ with forbidden subgroup $${{\mathbb {Z}}}_{2^k}$$ , can be obtained from spreads of $${{\mathbb {V}}}_n$$ for any $$k\le n/2$$ . In this article, existence and construction of bent functions from $${{\mathbb {V}}}_n$$ to $${{\mathbb {Z}}}_{2^k}$$ , which do not come from the spread construction is investigated. A construction of bent functions from $${{\mathbb {V}}}_n$$ into $${{\mathbb {Z}}}_{2^k}$$ , $$k\le n/6$$ , (and more generally, into any abelian group of order $$2^k$$ ) is obtained from partitions of $${{\mathbb {F}}}_{2^m}\times {{\mathbb {F}}}_{2^m}$$ , which can be seen as a generalization of the Desarguesian spread. As for the spreads, the union of a certain fixed number of sets of these partitions is always the support of a Boolean bent function.