Reviewerly/exHarmony huggingface dataset:
A Literature Review of Textual Hate Speech Detection Methods and Datasets

Online toxic discourses could result in conflicts between groups or harm to online communities. Hate speech is complex and multifaceted harmful or offensive content targeting individuals or groups. Existing literature reviews have generally focused on a particular category of hate speech, and to the best of our knowledge, no review has been dedicated to hate speech datasets. This paper systematically reviews textual hate speech detection systems and highlights their primary datasets, textual features, and machine learning models. The results of this literature review are integrated with content analysis, resulting in several themes for 138 relevant papers. This study shows several approaches that do not provide consistent results in various hate speech categories. The most dominant sets of methods combine more than one deep learning model. Moreover, the analysis of several hate speech datasets shows that many datasets are small in size and are not reliable for various tasks of hate speech detection. Therefore, this study provides the research community with insights and empirical evidence on the intrinsic properties of hate speech and helps communities identify topics for future work.
A Deep Learning Method for Breast Cancer Classification in the Pathology Images

Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems.
Blockchain-Based Secure Communication of Intelligent Transportation Digital Twins System

The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.
Benefits of physical and machine learning hybridization for photovoltaic power forecasting

Irradiance-to-power conversion is an essential step of state-of-the-art photovoltaic (PV) power forecasting, regardless of the source and post-processing of irradiance forecasts. The two distinct approaches for mapping the irradiance forecasts to PV power are physical and data-driven, which can also be hybridized. The contribution of this paper is twofold; first, it proposes a concept and identifies the best implementation of a hybrid physical and machine learning irradiance-to-power conversion method. Second, a head-to-head comparison of the physical, data-driven, and hybrid methods is performed for the operational day-ahead power forecasting of 14 PV plants in Hungary based on numerical weather prediction (NWP). To respect the rule of consistency but still obtain as complete picture as possible, two directives are set, namely minimizing the mean absolute error (MAE) and minimizing the root mean square error (RMSE), and separate sets of forecasts are optimized for both directives. The results reveal that for two years of training data, the hybrid method that involves the most physically-calculated predictors can reduce the MAE by 5.2% and 10.4% compared, respectively, to the optimized physical model chains and the machine learning without any physical considerations. The two most important physical modeling steps are separation and transposition modeling, and the rest of the physical PV simulation can be left to machine learning in hybrid models without a significant increase in the errors. The optimization of the physical model chains is found to be important even in the case of hybrid modeling; therefore, it should become a standard procedure in practical applications. Finally, the hybrid method is only beneficial for at least one year of training data, while in the initial period of the operation of a PV plant, it is advised to stay with optimized physical modeling. The guidelines and recommendations of this paper can help both researchers and practitioners design and optimize their power conversion model to increase the accuracy of the PV power forecasts.
ADOP

In this paper we present ADOP, a novel point-based, differentiable neural rendering pipeline. Like other neural renderers, our system takes as input calibrated camera images and a proxy geometry of the scene, in our case a point cloud. To generate a novel view, the point cloud is rasterized with learned feature vectors as colors and a deep neural network fills the remaining holes and shades each output pixel. The rasterizer renders points as one-pixel splats, which makes it very fast and allows us to compute gradients with respect to all relevant input parameters efficiently. Furthermore, our pipeline contains a fully differentiable physically-based photometric camera model, including exposure, white balance, and a camera response function. Following the idea of inverse rendering, we use our renderer to refine its input in order to reduce inconsistencies and optimize the quality of its output. In particular, we can optimize structural parameters like the camera pose, lens distortions, point positions and features, and a neural environment map, but also photometric parameters like camera response function, vignetting, and per-image exposure and white balance. Because our pipeline includes photometric parameters, e.g. exposure and camera response function, our system can smoothly handle input images with varying exposure and white balance, and generates high-dynamic range output. We show that due to the improved input, we can achieve high render quality, also for difficult input, e.g. with imperfect camera calibrations, inaccurate proxy geometry, or varying exposure. As a result, a simpler and thus faster deep neural network is sufficient for reconstruction. In combination with the fast point rasterization, ADOP achieves real-time rendering rates even for models with well over 100M points. https://github.com/darglein/ADOP
Improving sustainability in the tourism industry through blockchain technology: Challenges and opportunities

The tourism industry is extremely important to the world economy; yet, the industry falls short when it comes to economic, social, and environmental issues. Blockchain as an information technology can be utilized to help solve these issues and establish sustainable tourism globally. However, the challenges to blockchain adoption in the tourism industry have not yet been examined systematically. The goal of this study, therefore, is three-fold: we first identify the challenges to blockchain using literature review and expert opinions. Then, we examine them using the proposed rough Interpretive Structural Modeling - Cross-Impact Matrix Multiplication based on expert judgments. Finally, we link these challenges to diffusion of innovation theory. The results suggest that “lack of technical maturity” and “lack of interoperability” are the most important challenges of blockchain in the tourism industry. The findings of the study support macro- and micro-level decision-making in tourism industry's prospective applications of blockchain.
The Role of Blockchain in E-Governance and Decision-Making in Project and Program Management

This paper discusses the impact of e-governance powered by blockchain in the project and program management industry.With the rise in technological innovations, many countries have turned to e-governance for efficiency in service delivery, transparency, and decision-making.E-governance backed by blockchain technology entails improving the public services provision by implementing structures of information and communication technologies.There are many challenges with the traditional project management approach that causes organizations and its stakeholders' cost and time.Thus, the introduction of blockchain has given many organizations a new approach to adopt in order to eliminate the challenges associated with the typical project management approach.In spite of the cutting-edge technology of blockchain and its broad applications in program management and e-governance, there are still many challenges that restrain its adoption on a broader scale.The research discusses the challenges of the blockchain deployment in the program management field and e-governance in private and government sectors and it highlights the efforts put by both sectors to make use of the technology.Also, the research covers the opportunities and the advantages of a blockchain adoption in various segments.The paper uses various case studies in the UAE, in both private and government sectors, and a qualitative research approach was implemented.The case studies were on government entities such as Smart Dubai and the Ministry of Health Prevention and also private entities like DP World and Emirates NBD.The paper concludes with recommendations and solutions on how to maximize the benefit of blockchain in the program management industry and how it is improving the decision-making process.
Blockchain Technology for Supply Chains operating in emerging markets: an empirical examination of technology-organization-environment (TOE) framework

Abstract Organizations adopt blockchain technologies to provide solutions that deliver transparency, traceability, trust, and security to their stakeholders. In a novel contribution to the literature, this study adopts the technology-organization-environment (TOE) framework to examine the technological, organizational, and environmental dimensions for adopting blockchain technology in supply chains. This represents a departure from prior studies which have adopted the technology acceptance model (TAM), technology readiness index (TRI), theory of planned behavior (TPB), united theory of acceptance and use of technology (UTAUT) models. Data was collected through a survey of 525 supply chain management professionals in India. The research model was tested using structural equation modeling. The results show that all the eleven TOE constructs, including relative advantage, trust, compatibility, security, firm’s IT resources, higher authority support, firm size, monetary resources, rivalry pressure, business partner pressure, and regulatory pressure, had a significant influence on the decision of blockchain technology adoption in Indian supply chains. The findings of this study reveal that the role of blockchain technology adoption in supply chains may significantly improve firm performance improving transparency, trust and security for stakeholders within the supply chain. Further, this research framework contributes to the theoretical advancement of the existing body of knowledge in blockchain technology adoption studies.
CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization

For a long time, anomaly localization has been widely used in industries. Previous studies focused on approximating the distribution of normal features without adaptation to a target dataset. However, since anomaly localization should precisely discriminate normal and abnormal features, the absence of adaptation may make the normality of abnormal features overestimated. Thus, we propose Coupled-hypersphere-based Feature Adaptation (CFA) which accomplishes sophisticated anomaly localization using features adapted to the target dataset. CFA consists of (1) a learnable patch descriptor that learns and embeds target-oriented features and (2) scalable memory bank independent of the size of the target dataset. And, CFA adopts transfer learning to increase the normal feature density so that abnormal features can be clearly distinguished by applying patch descriptor and memory bank to a pre-trained CNN. The proposed method outperforms the previous methods quantitatively and qualitatively. For example, it provides an AUROC score of 99.5% in anomaly detection and 98.5% in anomaly localization of MVTec AD benchmark. In addition, this paper points out the negative effects of biased features of pre-trained CNNs and emphasizes the importance of the adaptation to the target dataset.
Cash Use Across Countries and the Demand for Central Bank Digital Currency

The level and trend in cash use in a country will influence the demand for central bank digital currency (CBDC).While access to digital currency will be more convenient than traveling to an ATM, it only makes CBDC like a bank debit card-not better.Demand for digital currency will thus be weak in countries where cash use is already very low, due to a preference for cash substitutes (cards, electronic money, mobile phone payments).Where cash use is very high, demand should be stronger, due to a lack of cash substitutes.As the demand for CBDC is tied to the current level of cash use, we estimate the level and trend in cash use for 11 countries using four different measures.A tentative forecast of cash use is also made.After showing that declining cash use is largely associated with demographic change, we tie the level of cash use to the likely demand for CBDC in different countries.In this process, we suggest that one measure of cash use is more useful than the others.If cash is important for monetary policy, payment instrument competition, or as an alternative payment instrument in the event of operational problems with privately supplied payment methods, the introduction of CBDC may best be introduced before cash substitutes become so ubiquitous that the viability of CBDC could be in doubt.
Graph-Flashback Network for Next Location Recommendation

Next Point-of Interest (POI) recommendation plays an important role in location-based applications, which aims to recommend the next POIs to users that they are most likely to visit based on their historical trajectories. Existing methods usually use rich side information, or customized POI graphs to capture the sequential patterns among POIs. However, the graphs only focus on connectivity between POIs. Few studies propose to explicitly learn a weighted POI graph, which could reflect the transition patterns among POIs and show the importance of its different neighbors for each POI. In addition, these approaches simply utilize the user characteristics for personalized POI recommendation without sufficient consideration. To this end, we construct a novel User-POI Knowledge Graph with strong representation ability, called Spatial-Temporal Knowledge Graph (STKG). STKG is used to learn the representations of each node (i.e., user, POI) and each edge. Then, we design a similarity function to construct our POI transition graph based on the learned representations. To incorporate the learned graph into sequential model, we propose a novel network Graph-Flashback for recommendation. Graph-Flashback applies a simplified Graph Convolution Network (GCN) on the POI transition graph to enrich the representation of each POI. Further, we define a similarity function to consider both spatiotemporal information and user preference in modelling sequential regularity. Experimental results on two real-world datasets show that our proposed method achieves the state-of-the-art performance and significantly outperforms all existing solutions.
E-commerce in high uncertainty avoidance cultures: The driving forces of repurchase and word-of-mouth intentions

This study proposes and empirically tests a contextualized model for investigating repurchase intentions of e-commerce consumers as well as their word-of-mouth (WOM) intentions in high uncertainty avoidance cultures and based on a synthesis of the S–O-R paradigm and Information Processing Theory (IPT). Data for the study were collected from 560 Jordanian online customers and analysed using structural equation modelling (PLS-SEM). The findings demonstrated that the dimensions of policy service quality including cash-on-delivery (COD) payment options and return policy leniency, positively influenced customer trust. In addition, electronic logistics service quality (LSQ) dimensions positively impacted customer satisfaction. Furthermore, it was found that satisfaction of online consumers is a major driver of their trust, repurchase, and WOM intentions. While customer trust was revealed as a key enabler of repurchase intention, its effect on WOM was insignificant. The findings provide useful implications for both practice and future research.
Golden Search Optimization Algorithm

This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called objects, which interact with each other based on a simple mathematical model to reach the global optimum. To provide a fine balance between the explorative and exploitative behavior of a search, the proposed method utilizes a transfer operator in the adaptive step size adjustment scheme. The proposed algorithm is benchmarked with 23 unimodal, multimodal, and fixed dimensional functions and the results are verified by a comparative study with the well-known Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Tunicate Swarm Algorithm (TSA). In addition, the nonparametric Wilcoxon&#x2019;s rank sum test is performed to measure the pair-wise statistical performance of the GSO and provide a valid judgment about the performance of the algorithm. The simulation results demonstrate that GSO is superior and could generate better optimal solutions when compared with other competitive algorithms.
Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry

Twitter being among the popular social media platforms, provide peoples’ opinions regarding specific ideas, products, services, etc. The large amounts of shared data as tweets can help extract users’ sentiment and provide valuable feedback to improve the quality of products and services alike. Similar to other service industries, the airline industry utilizes such feedback for determining customers’ satisfaction levels and improving the quality of experience where needed. This, of course, requires accurate sentiments from the user tweets. Existing sentiment analysis models suffer from low accuracy on account of the contradictions found in the tweet text and the assigned label. From this perspective, this study proposes a hybrid sentiment analysis approach where the lexicon-based methods are used with deep learning models to improve sentiment accuracy. Experiments involve analyzing the impact of TextBlob on the classification accuracy of models as against the original annotations, considering that the probability of the false annotations cannot be overlooked. Furthermore, the efficacy of TextBlob against Afinn and VADER (Valence Aware Dictionary for Sentiment Reasoning) is also evaluated. The CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and CNN-LSTM are deployed in comparison with state-of-the-art machine learning models. Additionally, the efficiency and efficacy of TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) are also investigated. Results suggest that models perform better when trained using the TextBlob assigned sentiments as compared to the original sentiments in the dataset. LSTM-GRU outperforms all models and previous studies with the highest 0.97 accuracy and 0.96 F1 scores. From machine learning models, the support vector classifier and extra tree classifier achieve the highest accuracy score of 0.92, with TF-IDF and BoW, respectively. Despite the good performance of the models using the TextBlob labels, TextBlob-based annotation cannot replace humans. Our stance is that with humans, bias, error-proneness, and subjectivity cannot be ignored; so we propose that the TextBlob-annotated labels can be used as assistance for human annotators where human annotators can wet the TextBlob-annotated dataset.
Explainable Artificial Intelligence in CyberSecurity: A Survey

Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being’s daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn’t satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a double-edged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking. Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.