Reviewerly/exHarmony huggingface dataset:
An Approach Towards the Design of Interval Type-3 T–S Fuzzy System

This article providesa systematic approach for the design of an interval type-3 (IT3) Takagi–Sugeno (T–S) fuzzy logic system (FLS) using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha $</tex-math></inline-formula> - plane representation. An IT3 FLS is designed with the baseline of the general type-2 (GT2) FLS in a similar manner as an IT2 FLS was designed from the baseline of type-1 FLS. Hence, IT3 FLS evolved as a successor of GT2 FLS, where secondary membership function is an interval type-2 fuzzy set (FS), and values of tertiary membership are unity over the footprint of uncertainty of secondary membership. This extra degree of freedom in IT3 FLS provides better modeling capability as compared to GT2 FLS in the presence of a high degree of uncertainty and vagueness. The proposed system will be more appealing while dealing with uncertain information or data, which is supposed to be generated from uncertain sources; i.e., there exist uncertainties even in the presence of uncertainty. The computations needed for the design of IT3 FLS are derived using IT2 FS and GT2 FS mathematics. The design algorithms adopted for the baseline IT2 T–S fuzzy system are as per the modified interval type-2 fuzzy c-regression model clustering algorithm and hyper-plane-shaped membership function. The proposed methodology is applied to several benchmark examples and obtained results are compared with recently developed fuzzy modeling methods having a comparable number of rule bases. The proposed IT3 T–S FLS shows good performance in terms of accuracy when data is corrupted by noise and uncertainties related to missing or unvarying data exist. The computational cost is linear with design parameters and by optimum choice of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha $</tex-math></inline-formula> -planes, it is still bearable considering advantages and nature of applications.
Ponzi Scheme Detection in Ethereum Transaction Network

With the rapid growth of blockchain, an increasing number of users have been attracted and many implementations have been refreshed in different fields. Especially in the cryptocurrency investment field, blockchain technology has shown vigorous vitality. However, along with the rise of online business, numerous fraudulent activities, e.g., money laundering, bribery, phishing, and others, emerge as the main threat to trading security. Due to the openness of Ethereum, researchers can easily access Ethereum transaction records and smart contracts, which brings unprecedented opportunities for Ethereum scams detection and analysis. This paper mainly focuses on the Ponzi scheme, a typical fraud, which has caused large property damage to the users in Ethereum. By verifying Ponzi contracts to maintain Ethereum’s sustainable development, we model Ponzi scheme identification and detection as a node classification task. In this paper, we first collect target contracts’ transactions to establish transaction networks and propose a detecting model based on graph convolutional network (GCN) to precisely distinguish Ponzi contracts. Experiments on different real-world Ethereum datasets demonstrate that our proposed model has promising results compared with general machine learning methods to detect Ponzi schemes.
Cursive Text Recognition in Natural Scene Images Using Deep Convolutional Recurrent Neural Network

Text recognition in natural scene images is a challenging problem in computer vision.Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions.In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes.Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text.The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features.Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels.To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results.To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed.The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures.The dataset is publicly available and can be downloaded from https://data.mendeley.com/datasets/k5fz57zd9z/1.
FastNet: Fast high-resolution network for human pose estimation

Aiming at the problem of developing efficient models for human pose estimation algorithms under computation-limited resources. In this paper, we proposed an efficient high-resolution network for human pose estimation, which named FastNet. First of all, The Lite-HRNet was constructed by using four parallel subnetworks. Each subnetwork contained multiple bottleneck block for feature extraction. The Lite-HRNet can effectively learn the high-resolution representation features of key points because of maintaining high-resolution. Secondly, instead of standard convolution, the asymmetric convolution was introduced to build an asymmetric bottleneck module. The asymmetric bottleneck module has different aspect ratios, and can be used to exact image features of keypoints with multi-scale characteristics and reduce the number of parameters. Thirdly, the waterfall module composed of multiple parallel convolutions were proposed to aggregates features with the same spatial size which can efficiently obtain delicate local representations. It retains rich spatial information and results in precise keypoint localization. Finally, the bottleneck blocks were replaced with asymmetric bottleneck modules in the third subnetwork of LiteHRNet. By which, The waterfall module is embedded into the structure of FastNet. Comprehensive experiments demonstrate that the proposed method achieves superior results on two benchmark datasets, MSCOCO and MPII. Moreover, FastNet demonstrates superior results on human pose estimation over popular lightweight networks.
Global Mask R-CNN for marine ship instance segmentation

Instance segmentation technology can provide accurate and efficient segmentation methods for visual perception of marine scenes, especially in the development of unmanned ships. However, the community lacks suitable open-source datasets. In order to address the problem of insufficient datasets, an instance segmentation dataset for the marine ship was collected and labeled. Our dataset, named MariShipInsSeg, consists of 4k high-quality visible light marine ship images with 8,413 instances. Due to marine ships being photographed far away, which causes ship objects with less detail information. Therefore, a global method is adopted to make full use of global location and semantic information, which is helpful for ship instance segmentation. We proposed a new method called Global Mask R-CNN (GM R-CNN), which utilized Precise RoI Pooling and Global Mask Head aiming to preserve global information of instances for improving the performance of ship instance segmentation. Experiments on the challenging MS COCO dataset and MariShipInsSeg dataset show that Global Mask R-CNN achieves state-of-the-art performance. Without any bells and whistles, the proposed GM R-CNN achieves 38.7% mask AP on MS COCO test-dev and 48.6% mask AP on MariShipInsSeg testing sets, which are gain of 1.6% and 1.9% compared with Mask R-CNN.
ForgeryNIR: Deep Face Forgery and Detection in Near-Infrared Scenario

Deep face forgery and detection is an emerging topic due to the development of GANs. Face forgery detection relies greatly on existing databases for evaluation and adequate training examples for data-hungry machine learning algorithms. However, considering the wide application of face recognition in near-infrared scenarios, there is no publicly available face forgery database that includes near-infrared modality currently. In this paper, we present an attempt at constructing a large-scale dataset for face forgery detection in the near-infrared modality and propose a new forgery detection method based on knowledge distillation named cross-modality knowledge distillation aiming to use a teacher model which is pre-trained on the visible light-based (VIS) big data to guide the student model with a small amount of near-infrared (NIR) data. The proposed near-infrared face forgery dataset, named ForgeryNIR, contains a total of over 50,000 real and fake identities. A number of perturbations are applied to help simulate real-world scenarios. All source images in ForgeryNIR are collected from CASIA NIR-VIS 2.0, and fake images are generated via multiple GAN techniques. The proposed dataset fills the gap of face forgery detection research in the near-infrared modality. A comprehensive study on six representative detection baselines is conducted to evaluate the performance of face forgery detection algorithms in the NIR domain. We further construct a hard testing set, named ForgeryNIR+, which contains forged images that have bypassed existing face forgery detection methods. The proposed datasets will be publicly available and aim to help boost further research on face forgery detection, as well as NIR face detection and recognition.
An integrated cyber security risk management framework and risk predication for the critical infrastructure protection

Cyber security risk management plays an important role for today's businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls. The framework is composed of a language, a process, and it is supported by an automated tool. The paper also reports on the evaluation of our work to a real case study of a critical infrastructure. The results reveal that using the fuzzy set theory in assessing assets' criticality, our work supports stakeholders towards an effective risk management by assessing each asset's criticality. Furthermore, the results have demonstrated the machine learning classifiers' exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware.
Multi-polarization fusion generative adversarial networks for clear underwater imaging

Polarization imaging has become a promising way for clear underwater vision, which depends on the difference of polarization characteristics between backscattered light and target signal. In this paper, to achieve clear underwater color polarization imaging, we propose a learning-based method that uses multi-polarization fusion adversarial generative networks to learn the relationship between polarization information and object radiance. The proposed method is especially designed for multiple polarimetric images, which can effectively extract the polarization correlations of images with different polarization states. Moreover, to train the proposed network, we build, for the first time to our knowledge, a color polarization image dataset from natural underwater environments through passive polarization imaging. The experimental results in laboratory and natural underwater environments show that it is feasible to introduce polarization information into learning-based image recovery, and deep learning technology is conducive to the extraction of polarization information. Comparing with other methods, the proposed method can effectively remove the backscattered light and recover the object radiance.
InfraGAN: A GAN architecture to transfer visible images to infrared domain

Utilizing both visible and infrared (IR) images in various deep learning based computer vision tasks has been a recent trend. Consequently, datasets having both visible and IR image pairs are desired in many applications. However, while large image datasets taken at the visible spectrum can be found in many domains, large IR-based datasets are not easily available in many domains. The lack of IR counterparts of the available visible image datasets limits existing deep algorithms to perform on IR images effectively. In this paper, to overcome with that challenge, we introduce a generative adversarial network (GAN) based solution and generate the IR equivalent of a given visible image by training our deep network to learn the relation between visible and IR modalities. In our proposed GAN architecture (InfraGAN), we introduce using structural similarity as an additional loss function. Furthermore, in our discriminator, we do not only consider the entire image being fake or real but also each pixel being fake or real. We evaluate our comparative results on three different datasets and report the state of the art results over five metrics when compared to Pix2Pix and ThermalGAN architectures from the literature. We report up to +16% better performance in Structural Similarity Index Measure (SSIM) over Pix2Pix and +8% better performance over ThermalGAN for VEDAI dataset. Further gains on different metrics and on different datasets are also reported in our experiments section.
Protecting Location Privacy of Users Based on Trajectory Obfuscation in Mobile Crowdsensing

In mobile crowdsensing activities, it is usually necessary for participants to upload sensing data and related locations. The existing location privacy-preserving mechanisms cannot well protect a user's trajectory privacy because attackers can mine the user's trajectory features through data analysis techniques. Aiming at the trajectory privacy protection problem, this article proposes a differential location privacy-preserving mechanism based on trajectory obfuscation (LPMT). LPMT first extracts the stay points as the features of a trajectory based on the sliding window algorithm, and then obfuscates each stay point to a target obfuscation subregion through the exponential mechanism, and finally performs the Laplace sampling in the target obfuscation subregion to obtain the obfuscated GPS points. Compared with the baseline mechanisms, LPMT can reduce data quality loss by more than 20% while providing the same level of obfuscation quality, which indicates that LPMT has the advantages of strong security and high quality of service.
Blockchain-Based AI-Enabled Industry 4.0 CPS Protection Against Advanced Persistent Threat

Industry 4.0 is all about doing things in a concurrent, secure, and fine-grained manner. IoT edge-sensors and their associated data play a predominant role in today's industry ecosystem. Breaching data or forging source devices after injecting advanced persistent threats (APT) damages the industry owners' money and loss of operators' lives. The existing challenges include APT injection attacks targeting vulnerable edge devices, insecure data transportation, trust inconsistencies among stakeholders, incompliant data storing mechanisms, etc. Edge-servers often suffer because of their lightweight computation capacity to stamp out unauthorized data or instructions, which in essence, makes them exposed to attackers. When attackers target edge servers while transporting data using traditional PKI-rendered trusts, consortium blockchain (CBC) offers proven techniques to transfer and maintain those sensitive data securely. With the recent improvement of edge machine learning, edge devices can filter malicious data at their end which largely motivates us to institute a Blockchain and AI aligned APT detection system. The unique contributions of the paper include efficient APT detection at the edge and transparent recording of the detection history in an immutable blockchain ledger. In line with that, the certificateless data transfer mechanism boost trust among collaborators and ensure an economical and sustainable mechanism after eliminating existing certificate authority. Finally, the edge-compliant storage technique facilitates efficient predictive maintenance. The respective experimental outcomes reveal that the proposed technique outperforms the other competing systems and models.
Text sentiment analysis of fusion model based on attention mechanism

Text sentiment tendency analysis is a hot task in natural language processing. And text as the essential expression form of language, both individual word information, and overall utterance, deserves to be focused on. This paper proposes a fusion model to achieve high precision text sentiment analysis. This model combines the advantages of CNN to extract local information of text and BiLSTM to extract contextual association of text and introduces the attention mechanism to increase the focus on words with a solid emotional tendency in the text. The training datasets are comments that crawled from several social media sites such as Facebook, Twitter, Instagram, WhatsApp, etc. Based on the attention mechanism, this paper investigates the semantic sentiment analysis to reach the study of classification prediction for analyzing the positive and negative sentiment of financial news, social media, etc. The experimental results show that the proposed method can better extract features from the text and classify them than other baseline models.
Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

Social media websites are becoming more prevalent on the Internet. Sites, such as Twitter, Facebook, and Instagram, spend significantly more of their time on users online. People in social media share thoughts, views, and facts and create new acquaintances. Social media sites supply users with a great deal of useful information. This enormous quantity of social media information invites hackers to abuse data. These hackers establish fraudulent profiles for actual people and distribute useless material. The material on spam might include commercials and harmful URLs that disrupt natural users. This spam content is a massive problem in social networks. Spam identification is a vital procedure on social media networking platforms. In this paper, we have proposed a spam detection artificial intelligence technique for Twitter social networks. In this approach, we employed a vector support machine, a neural artificial network, and a random forest technique to build a model. The results indicate that, compared with RF and ANN algorithms, the suggested support vector machine algorithm has the greatest precision, recall, and F-measure. The findings of this paper would be useful in monitoring and tracking social media shared photos for the identification of inappropriate content and forged images and to safeguard social media from digital threats and attacks.
A hybrid extreme learning machine model with harris hawks optimisation algorithm: an optimised model for product demand forecasting applications

Abstract Accurate and real-time product demand forecasting is the need of the hour in the world of supply chain management. Predicting future product demand from historical sales data is a highly non-linear problem, subject to various external and environmental factors. In this work, we propose an optimised forecasting model - an extreme learning machine (ELM) model coupled with the Harris Hawks optimisation (HHO) algorithm to forecast product demand in an e-commerce company. ELM is preferred over traditional neural networks mainly due to its fast computational speed, which allows efficient demand forecasting in real-time. Our ELM-HHO model performed significantly better than ARIMA models that are commonly used in industries to forecast product demand. The performance of the proposed ELM-HHO model was also compared with traditional ELM, ELM auto-tuned using Bayesian Optimisation (ELM-BO), Gated Recurrent Unit (GRU) based recurrent neural network and Long Short Term Memory (LSTM) recurrent neural network models. Different performance metrics, i.e., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Percentage Error (MPE) were used for the comparison of the selected models. Horizon forecasting at 3 days and 7 days ahead was also performed using the proposed approach. The results revealed that the proposed approach is superior to traditional product demand forecasting models in terms of prediction accuracy and it can be applied in real-time to predict future product demand based on the previous week’s sales data. In particular, considering RMSE of forecasting, the proposed ELM-HHO model performed 62.73% better than the statistical ARIMA(7,1,0) model, 40.73% better than the neural network based GRU model, 34.05% better than the neural network based LSTM model, 27.16% better than the traditional non-optimised ELM model with 100 hidden nodes and 11.63% better than the ELM-BO model in forecasting product demand for future 3 months. The novelty of the proposed approach lies in the way the fast computational speed of ELMs has been combined with the accuracy gained by tuning hyperparameters using HHO. An increased number of hyperparameters has been optimised in our methodology compared to available models. The majority of approaches to improve the accuracy of ELM so far have only focused on tuning the weights and the biases of the hidden layer. In our hybrid model, we tune the number of hidden nodes, the number of input time lags and even the type of activation function used in the hidden layer in addition to tuning the weights and the biases. This has resulted in a significant increase in accuracy over previous methods. Our work presents an original way of performing product demand forecasting in real-time in industry with highly accurate results which are much better than pre-existing demand forecasting models.