Reviewerly/exHarmony huggingface dataset:
Insurance fraud detection: Evidence from artificial intelligence and machine learning

This study proposes a framework for fraud detection in the auto insurance industry by using predictive models. The feature selection is performed utilizing a publicly available car insurance dataset and uncovers the most influential feature through Boruta algorithm. Three predictive models (logistic regression, support vector machine, and naïve Bayes) are applied for developing a fraud detection mechanism. Six metrics are computed from the confusion matrix to assess the performance of the predictive model. The results reveal that the support vector machine outperforms in terms of accuracy, and the logistic regression achieves the highest f-measure score. Each influential feature's ranking is performed, and it is revealed that the fault, base policy, and age of the policyholder are the most influential features. The findings of this study are beneficial for fraud detection in the auto insurance industry. Additionally, the underlying framework holds a functionality for real-time problem-solving in the auto insurance industry.
Emperor penguin optimizer: A comprehensive review based on state-of-the-art meta-heuristic algorithms

Meta heuristics is an optimization approach that works as an intelligent technique to solve optimization problems. Evolutionary algorithms, human-based algorithms, physics-based algorithms and swarm intelligence are categorized under meta-heuristic algorithms. This study presents a critical review of meta-heuristic algorithms for future reference, including concepts, applications, advantages and disadvantages, before focusing on one specific meta-heuristic algorithm, namely, Emperor Penguin Optimizer (EPO). It is an intelligent algorithm developed after observing the behaviour of emperor penguins during cold winters. This technique was introduced by Dhiman in 2018 and adopted to solve optimization problems. The study reviews the algorithm variants starting from its invention in 2018 until 2022. The literature is comprehensively reviewed to reflect on the progress of the algorithm’s adoption, highlighting a new area for improvement. The most significant result is that the proposed algorithm has been proven an effective technique. The merits and demerits of the algorithm are explored to provide valuable perspectives for future research. This study answers the question regarding meta-heuristic algorithms’ effectiveness, especially EPO. Both beginners and experts of EPO research can use the findings of this study as guidelines for enhancing current concepts and applications of state-of-the-art algorithms for future development works.
The impacts of blockchain adoption on a dual-channel supply chain with risk-averse members

Blockchain technology is believed to leverage the potential of supply chain performance. However, its benefits and related costs need to be balanced when implementing blockchain solutions. This study constructs a dual-channel supply chain where a manufacturer sells products through both the direct sales channel and the retail channel. Three blockchain adoption scenarios are considered in this study. We analytically find the blockchain adoption strategies of supply chain members are dependent on unit blockchain-operational cost, direct selling cost, and demand volatility. When blockchain adoption is cost-effective and demand volatility is small and medium, the adoption of blockchain solutions by both the manufacturer and the e-retailer (Model B) is more beneficial for the individual and the supply chain, but if demand volatility is large, it is more advantageous when only the e-retailer initiates blockchain solutions (Model R). When blockchain adoption is costly and demand volatility is medium and large, it is more valuable when only the manufacturer initiates blockchain solutions (Model D). We also conduct numerical experiments to inspect the impacts of an imperfectly competitive market and the variation of stakeholders’ risk-averse level on the model to present the robustness of our results. Based on these findings, we further provide theoretical and practical guidance for supply chain managers in determining the optimal blockchain adoption scenarios in a dual-channel supply chain.
A cluster of 1D quadratic chaotic map and its applications in image encryption

Chaotic systems are widely used in designing encryption algorithms for their ideal dynamical performances. One-dimensional (1D) chaotic maps have the highest efficiency in implementation and have achieved great attention. However, 1D chaotic maps have a common security weakness, which is that their key space is relatively small. Therefore, in this paper, a cluster of 1D quadratic chaotic maps is proposed according to the topological conjugate theory. The 1D chaotic map has three tunable parameters which have a significant expansion in parameter space than the traditional 1D chaotic maps. The 1D map is proved to be chaotic theoretically since it is topologically conjugated with a logistic chaotic map. An example of a 1D quadratic chaotic map is provided in this paper, and several numerical simulation results indicate that this 1D quadratic map has ideal chaotic characteristics, which are consistent with the theoretical analysis. To verify the effectiveness of this 1D quadratic map, a novel image encryption algorithm is proposed. The security of this image encryption algorithm is completely dependent on the properties of the 1D quadratic map. Security experimental test results show that this image encryption algorithm has a high-security level, and is quite competitive with other chaos-based image encryption algorithms.
Temporal-Spatial Quantum Graph Convolutional Neural Network Based on Schrödinger Approach for Traffic Congestion Prediction

Traffic congestion prediction (TCP) plays a vital role in intelligent transportation systems due to its importance of traffic management. Methods for TCP have emerged greatly with the development of machine learning. However, TCP is always a challenging work due to the dynamic characteristics of traffic data and the complex structure of traffic network. This paper presents a new quantum algorithm that can capture temporal and spatial features of traffic data simultaneously for TCP. The algorithm consists of the following steps. First, we give a closed-form solution in the Schrödinger approach theoretically to analyze this TCP problem in time dimension. Then we can get the temporal features from the solution. At last, we construct a quantum graph convolutional network and apply temporal features into it. Thus, the temporal-spatial quantum graph convolutional neural network is proposed. The feasibility of this method is proved through experiments on the simulation platform. The experimental results show the average error rate is 0.21 and can resist perturbation effectively.
Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision

Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis, treatment planning and following-up. Collecting and annotating a large-scale dataset is crucial to training a powerful segmentation model, but producing high-quality segmentation masks is an expensive and time-consuming procedure. Recently, weakly-supervised learning that uses sparse annotations (points, scribbles, bounding boxes) for network training has achieved encouraging performance and shown the potential for annotation cost reduction. However, due to the limited supervision signal of sparse annotations, it is still challenging to employ them for networks training directly. In this work, we propose a simple yet efficient scribble-supervised image segmentation method and apply it to cardiac MRI segmentation. Specifically, we employ a dual-branch network with one encoder and two slightly different decoders for image segmentation and dynamically mix the two decoders' predictions to generate pseudo labels for auxiliary supervision. By combining the scribble supervision and auxiliary pseudo labels supervision, the dual-branch network can efficiently learn from scribble annotations end-to-end. Experiments on the public ACDC dataset show that our method performs better than current scribble-supervised segmentation methods and also outperforms several semi-supervised segmentation methods. Code is available: https://github.com/HiLab-git/WSL4MIS .
Multiscale Progressive Segmentation Network for High-Resolution Remote Sensing Imagery

Semantic segmentation of high-resolution remote sensing imageries (HRSIs) is a critical task for a wide range of applications, such as precision agriculture and urban planning. Although convolutional neural networks (CNNs) have made great progress in accomplishing this task recently, there still exist some challenges to address, one of which is simultaneously segmenting objects with large scale variations in a HRSI. Targeting at this challenge, previous CNNs often adopt multiple convolution kernels in one layer or skip-layer connections between different layers to extract multiscale representations. However, due to the limited learning capacity of each CNN, it tends to make trade-offs in segmenting different-scale objects. This would lead to unsatisfactory segmentation results for some objects, especially the small or the large ones. In this paper, we propose a multiscale progressive segmentation network to address this issue. Instead of forcing one network to deal with all scales of objects, our network attempts to cascade three subnetworks for gradually segmenting objects with small scales, large scales, and other scales. In order to make the subnetwork focus on the specific scale objects, a scale guidance module is designed. It takes advantage of segmentation results from the preceding subnetwork to guide the feature learning of the succeeding one. Additionally, to acquire the final segmentation results, we propose a position sensitive module for adaptively combining the outputs of the three subnetworks. This module is capable of assigning combination weights of different subnetworks according to their importance. Experiments on two benchmark datasets named Vaihingen and Potsdam indicate that our proposed network can achieve considerable improvements in comparison with several state-of-the-art segmentation models.
Cultural Heritage in Fully Immersive Virtual Reality

Fully immersive virtual reality (VR) applications have modified the way people access cultural heritage—from the visiting of virtual museums containing large collections of paintings to the visiting of ancient buildings. In this paper, we propose to review the software that are currently available that deal with cultural heritage in fully immersive virtual reality. It goes beyond technologies that were available prior to virtual reality headsets, at a time where virtual was simply the synonym of the application of digital technologies to cultural heritage. We propose to group these applications depending on their content—from generic art galleries and museums to applications that focus on a single artwork or single artist. Furthermore, we review different ways to assess the performance of such applications with workload, usability, flow, and potential VR symptoms surveys. This paper highlights the progress in the implementation of applications that provide immersive learning experiences related to cultural heritage, from 360 images to photogrammetry and 3D models. The paper shows the discrepancy between available software to the general audience on various VR headsets and scholarship activities dealing with cultural heritage in VR.
Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review

Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes popular ML techniques used for fraud detection, the most popular fraud type, and evaluation metrics. The reviewed articles showed that support vector machine (SVM) and artificial neural network (ANN) are popular ML algorithms used for fraud detection, and credit card fraud is the most popular fraud type addressed using ML techniques. The paper finally presents main issues, gaps, and limitations in financial fraud detection areas and suggests possible areas for future research.
Building PV integration according to regional climate conditions: BIPV regional adaptability extending Köppen-Geiger climate classification against urban and climate-related temperature increases

Despite the technical maturity and substantial potential cost reduction of BIPV technologies, there are still challenges to overcome for the expansion of BIPV applications and their wider adaptation at global level. Among these, the alignment of PV integration with particular climate and environmental conditions of the local solar architecture is crucial. This will facilitate the transition to sustainable buildings and the mitigation of climate change. In this context, this study proposes for the first time, a novel BIPV climatic design framework for PV buildings positioning and adaptation to local climate towards the minimization of energy expenditure and use of resources. With the review and analysis of a large numbers of BIPV studies globally for seventy parameters grouped in eight main categories of an open-access database, the global horizontal irradiation (GHI) value is selected as an additional index to the Köppen-Geiger classification scheme. The extension accounts for the urban suitability and vulnerability and prioritize the building integration of photovoltaics. Four zones of cold (low GHI), moderate (medium GHI), warm (high GHI) and hot (very high GHI) climatic regions are considered and applied for 127 cities globally. In this framework, the sequence of PV building component integration is proposed according to local climate of each zone and the energy performance of buildings is maximized towards their positive energy contributions and sharing in local, district and city grids. Barriers and limitations of the BIPV implementation at a larger scale are discussed and the emerging research needs are revealed.
A Novel Deep Learning Approach for Deepfake Image Detection

Deepfake is utilized in synthetic media to generate fake visual and audio content based on a person’s existing media. The deepfake replaces a person’s face and voice with fake media to make it realistic-looking. Fake media content generation is unethical and a threat to the community. Nowadays, deepfakes are highly misused in cybercrimes for identity theft, cyber extortion, fake news, financial fraud, celebrity fake obscenity videos for blackmailing, and many more. According to a recent Sensity report, over 96% of the deepfakes are of obscene content, with most victims being from the United Kingdom, United States, Canada, India, and South Korea. In 2019, cybercriminals generated fake audio content of a chief executive officer to call his organization and ask them to transfer $243,000 to their bank account. Deepfake crimes are rising daily. Deepfake media detection is a big challenge and has high demand in digital forensics. An advanced research approach must be built to protect the victims from blackmailing by detecting deepfake content. The primary aim of our research study is to detect deepfake media using an efficient framework. A novel deepfake predictor (DFP) approach based on a hybrid of VGG16 and convolutional neural network architecture is proposed in this study. The deepfake dataset based on real and fake faces is utilized for building neural network techniques. The Xception, NAS-Net, Mobile Net, and VGG16 are the transfer learning techniques employed in comparison. The proposed DFP approach achieved 95% precision and 94% accuracy for deepfake detection. Our novel proposed DFP approach outperformed transfer learning techniques and other state-of-the-art studies. Our novel research approach helps cybersecurity professionals overcome deepfake-related cybercrimes by accurately detecting the deepfake content and saving the deepfake victims from blackmailing.
Green investment in a sustainable supply chain: The role of blockchain and fairness

• The manufacturer’s incentive to adopt blockchain depends on a not sufficiently high operational cost. • Fairness can encourage blockchain implementation if and only if the blockchain-driven increment in customers’ green sensitivity level is nominal. • Blockchain adoption may weaken the positive impact of fairness on the supply chain performance. • Blockchain adoption can help achieve an “all-win” situation for the supply chain. We study a green investment problem of a sustainable supply chain with one manufacturer who decides whether to implement blockchain and one retailer who has emotional fairness concerns. Although blockchain adoption is costly, it can raise customers’ green sensitivity level and exempt a firm from environmental taxes. A fair-minded retailer cares about disadvantageous inequity in addition to monetary payoffs. We compare the supply chain performance with and without blockchain, each in combination with and without emotional fairness concerns. We show that the manufacturer has the incentive to adopt blockchain if the operational cost is below a certain threshold, and this threshold increases in both tax rate and the blockchain-driven increment in customers’ green sensitivity. We find that fairness can encourage blockchain implementation if and only if the blockchain-driven increment in customers’ green sensitivity level is nominal. Moreover, blockchain adoption may weaken the positive impact of fairness on the supply chain performance such as the mitigation of double marginalization and consumer welfare improvement. We also find that blockchain can encourage green investment and benefit the retailer only if the blockchain-driven increment in customers’ green sensitivity level is relatively high; that is, an “all-win” situation for the supply chain can be achieved by blockchain adoption. Extended studies on consumption-based emission taxes and blockchain setup cost are also provided.
DiT: Self-supervised Pre-training for Document Image Transformer

Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose DiT, a self-supervised pre-trained Document Image Transformer model using large-scale unlabeled text images for Document AI tasks, which is essential since no supervised counterparts ever exist due to the lack of human-labeled document images. We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, table detection as well as text detection for OCR. Experiment results have illustrated that the self-supervised pre-trained DiT model achieves new state-of-the-art results on these downstream tasks, e.g. document image classification (91.11 - 92.69), document layout analysis (91.0 - 94.9), table detection (94.23 - 96.55) and text detection for OCR (93.07 - 94.29). The code and pre-trained models are publicly available at https://aka.ms/msdit.
Aspect-level sentiment analysis: A survey of graph convolutional network methods

Aspect-level sentiment analysis (ALSA) is the process of collecting, processing, analyzing, inferring, and synthesizing subjective sentiments of entities contained in texts at the aspect level. The development of social networks has been driven by the on-going appearance of vast numbers of short documents, such as those in which opinions are expressed and comments are made. The text in these documents reflects users’ emotions related to entities. The ALSA of these short texts plays an important role in solving various problems in life. Particularly in e-commerce, manufacturers can use sentiment analysis to determine users’ orientations, adapt their products to perfection, identify potential users, and pinpoint users that influence other users. Therefore, improving the performance of ALSA methods has recently attracted the interest of researchers. Currently, four main types of ALSA methods are available: knowledge-based, machine learning-based, hybrid-based, and most recently, graph convolutional network (GCN)-based. This study is the first survey to focus on reviewing the proposed methods for ALSA using GCN methods. In this paper, we propose a novel taxonomy to divide GCN-based ALSA models into three categories based on the types of knowledge extraction. We present and compare GCN-based ALSA methods following our taxonomy comprehensively. Common benchmark datasets and text representations that are often used in GCN-based methods are also discussed. In addition, we discuss five challenges and suggest seven future research directions for GCN-based ALSA methods. The findings of our survey are expected to provide the necessary guidelines for beginners, practitioners, and new researchers to improve the performance of ALSA methods.
SemiACO: A semi-supervised feature selection based on ant colony optimization

Feature selection is one of the most efficient procedures for reducing the dimensionality of high-dimensional data by choosing a practical subset of features. Since labeled samples are not always available and labeling data may be time-consuming or costly, the importance of semi-supervised learning becomes apparent. Semi-supervised learning deals with data that includes both labeled and unlabelled instances. This article proposes a method based on Ant Colony Optimization (ACO) for the semi-supervised feature selection problem called SemiACO. The SemiACO algorithm finds features by considering the minimum redundancy between features and the maximum relevancy between the features and the class label. The SemiACO uses a nonlinear heuristic function instead of a linear one. The heuristic learning technique for the ACO heuristic function utilize a Temporal Difference (TD) reinforcement learning algorithm. We characterize the feature selection search space as a Markov Decision Process (MDP), where features indicate the states, and selecting the unvisited features by each ant represents a set of actions. We contrast the efficiency of SemiACO based on various experiments on 14 benchmark datasets, comparing eight semi-supervised feature selection methods.
Intelligent Breast Abnormality Framework for Detection and Evaluation of Breast Abnormal Parameters

Unlike the healthy cells in the breast tissue, cancerous breast cells are unwelcome and have strange properties. In both sexes, this will quickly expand and infiltrate adjacent tissue, leading to the formation of a tumour. Using the Intelligent-Breast Abnormality Detection (I-BAD) framework, many breast cancer parameters are evaluated in this article. It has already been shown that some indicators may be used for early detection of breast cancer. There is also discussion of the instruments and strategies that facilitate the monitoring of the selected breast health metrics. Classification methods that use machine learning to store and analyse data are also discussed. The suggested I-BAD framework’s process is then visually shown in clean drawings.