Reviewerly/exHarmony huggingface dataset:
Creation of sustainable growth with explainable artificial intelligence: An empirical insight from consumer packaged goods retailers

Consumer packaged goods retailers can pivot quickly to find new areas of sustainable growth and enable their businesses to compete effectively. Traditionally, they are losing ground to more agile and technologically focused competitors who quickly adapt their operations to shifting market conditions. As a result, such retailers must alter their core technologies. Explainable artificial intelligence is a set of procedures and strategies that allow machine learning algorithms and artificial intelligence models’ results and output to understand and trust. In the era of sustainable digitalisation, there is lack of research on the creation of sustainable growth with explainable artificial intelligence. Therefore, this study is undertaken to uncover how consumer packaged goods retailers can create sustainable growth with explainable artificial intelligence by proposing a unique conceptual model. The source data were collected from 310 individuals from consumer packaged goods retailers and the analysis was performed with quantitative research methodology. The findings suggest that explainable artificial intelligence has the potential to be a formidable weapon in the fight for sustainable growth. With the employment of automated machines, the best decisions for sustainable competitive advantage can be made. In addition, explainable artificial intelligence-powered automated wealth management services and algorithmic trading help to make better financial decisions. The theoretical implications argue the explainability of artificial intelligence systems to offer insights for making impactful decisions. The managerial implications highlight the benefits of the adoption of explainable artificial intelligence for sustainable growth, such as increase in trust, complying with legal regulations and providing data-driven actionable analytical insights. Sustainable growth set more responsibility towards customers for profitable future.
BAuth-ZKP—A Blockchain-Based Multi-Factor Authentication Mechanism for Securing Smart Cities

The overwhelming popularity of technology-based solutions and innovations to address day-to-day processes has significantly contributed to the emergence of smart cities. where millions of interconnected devices and sensors generate and share huge volumes of data. The easy and high availability of rich personal and public data generated in these digitalized and automated ecosystems renders smart cities vulnerable to intrinsic and extrinsic security breaches. Today, with fast-developing technologies, the classical username and password approaches are no longer adequate to secure valuable data and information from cyberattacks. Multi-factor authentication (MFA) can provide an effective solution to minimize the security challenges associated with legacy single-factor authentication systems (both online and offline). This paper identifies and discusses the role and need of MFA for securing the smart city ecosystem. The paper begins by describing the notion of smart cities and the associated security threats and privacy issues. The paper further provides a detailed description of how MFA can be used for securing various smart city entities and services. A new concept of blockchain-based multi-factor authentication named "BAuth-ZKP" for securing smart city transactions is presented in the paper. The concept focuses on developing smart contracts between the participating entities within the smart city and performing the transactions with zero knowledge proof (ZKP)-based authentication in a secure and privacy-preserved manner. Finally, the future prospects, developments, and scope of using MFA in smart city ecosystem are discussed.
Communication and computation efficiency in Federated Learning: A survey

Federated Learning is a much-needed technology in this golden era of big data and Artificial Intelligence, due to its vital role in preserving data privacy, and eliminating the need to transfer and process huge amounts of data, while maintaining the numerous benefits of Machine Learning. As opposed to the typical central training process, Federated Learning involves the collaborative training of statistical models by exchanging learned parameter updates. However, wide adoption of the technology is hindered by the communication and computation overhead forming due to the demanding computational cost of training, and the large-sized parameter updates exchanged. In popular applications such as those involving Internet of Things, the effects of the overhead are exacerbated due to the low computational prowess of edge and fog devices, limited bandwidth, and data capacity of internet connections. Over the years, many research activities that target this particular issue were conducted but a comprehensive review of the fragmented literature is still missing. This paper aims at filling this gap by providing a systematic review of recent work conducted to improve the communication and/or computation efficiency in Federated Learning. We begin by introducing the essentials of Federated Learning and its variations, followed by the literature review placed according to an encompassing, easy-to-follow taxonomy. Lastly, the work sheds light on the current challenges faced by the technology and possible directions for future work.
PCAS: Cryptanalysis and improvement of pairing-free certificateless aggregate signature scheme with conditional privacy-preserving for VANETs

Certificateless aggregate signature (CLAS) is an effective approach for the multiple authentications in multi-users and multi-information environments like the vehicular ad hoc networks (VANETs), which can address the certificate management issue in the traditional public key cryptography (PKC) and solve the key escrow problem in identity-based cryptography (IBC). In recent years, a new CLAS mechanism (LICLAS) is presented for the authentication of sensors in healthcare wireless medical sensor networks (HWMSNs). In this paper, we make an analysis and prove that LICLAS is not able to against forgery attacks. Thus, we present an improved CLAS scheme (PCAS) for vehicle authentication in VANETs, which satisfies the privacy and security requirements. PCAS constructs the signature algorithm based on Elliptic Curve Cryptography (ECC) to avoid bilinear pairing and Map-to-Hash function operations. In order to further improve security and efficiency of PCAS, the vehicle key generation and signature algorithm is revised. The pseudonym mechanism is also adopted to achieve conditional privacy preservation. Besides, PCAS allows to execute a batch verification of messages, where RSUs may verify the aggregated signatures from vehicles to reduce time overhead. Under the assumption that the elliptic curve discrete logarithm problem (ECDLP) is hard, PCAS is proved to be existentially unforgeable against adaptive chosen message attacks in the random oracle model. Through the performance analysis, under the same time complexity, PCAS is shown to more effective. Compared with LICLAS, for a single message and 2000 messages, the transmission overhead of PCAS is reduced by 25% and 25% respectively, and the computation overhead is reduced by 16.56% and 25.34% respectively.
A Survey on Web Application Penetration Testing

Websites are becoming increasingly effective communication tools. Nevertheless, web applications are vulnerable to attack and can give attackers access to sensitive information or unauthorized access to accounts. The number of vulnerabilities in web applications has increased dramatically over the past decade. Many are due to improper validation and sanitization of input. Identifying these vulnerabilities is essential for developing high-quality, secure web applications. Whenever a website is released to the public, it is required to have had penetration testing to a certain standard to ensure the security of the information. Application-level security vulnerability detection is possible for many commercial and open-source applications. However, developers are curious about which tools detect security vulnerabilities and how quickly they do so. The purpose of this study is to discuss penetration testing and how it can be implemented. This paper also explores the hazards and vulnerabilities associated with the web environment as well as the protective measures that can be taken. In addition, a comprehensive review and comparison of common web penetration testing tools is provided. The aim of this paper is to help web penetration testers choose a technology that is optimal for their requirements. The paper also sets out to guide and provide recommendations to users for choosing the best web penetration test tool and increasing their awareness of secure web environments. The study results indicate that not all web penetration testing tools offer the same features and that combining analysis tools can provide detailed information about web vulnerabilities.
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity. To fill this gap, in this paper, we propose a novel FL platform, named FederatedScope, which employs an event-driven architecture to provide users with great flexibility to independently describe the behaviors of different participants. Such a design makes it easy for users to describe participants with various local training processes, learning goals and backends, and coordinate them into an FL course with synchronous or asynchronous training strategies. Towards an easy-to-use and flexible platform, FederatedScope enables rich types of plug-in operations and components for efficient further development, and we have implemented several important components to better help users with privacy protection, attack simulation and auto-tuning. We have released FederatedScope at https://github.com/alibaba/FederatedScope to promote academic research and industrial deployment of federated learning in a wide range of scenarios.
High-resolution PV power prediction model based on the deep learning and attention mechanism

Photovoltaic (PV) power generation is associated with volatility and randomness due to susceptibility to meteorological parameters intermittency. This poses difficulty in achieving the desired accuracy of PV power prediction with traditional models. Thus, this paper proposes a new predictive model based on deep learning techniques, optimized by the Bayesian optimization algorithm, to forecast a day-ahead PV power generation in high-resolution time steps. A systematic algorithm is introduced to improve time-series data quality via identifying missing samples in high-frequency datasets and imputing the missing values through the LASSO regression technique. The two data transformers for time and wind features are proposed to enhance their contributions, while other weather information, such as temperature and humidity, are considered. The proposed hybrid model incorporates CNN and BiLSTM to learn spatial and temporal patterns; moreover, the attention mechanism determines the weight values for input series and puts explicit attention on more essential parts to improve accuracy. Finally, the performance of the proposed model is compared with nine deep learning models, which are all optimized by the Bayesian optimization technique. The prediction performance comparison on actual data for a year reveals the superiority of the proposed model with the overall performance of 0,247, 0,232, 1,58%, and 0,461 in MAE, MSE, MAPE, and RMSE, respectively.
PN-GCN: Positive-negative graph convolution neural network in information system to classification

Graph convolution neural network (GCN) shows strong performance in non-Euclidean structure data. In recent years, many researchers have applied GCN to Euclidean structure data, such as images and languages, and obtained excellent results, which expanded the application range of GCN. Despite a large number of related methods, few studies deal with information systems from the perspective of graphs. In fact, the information system is also a non-Euclidean data structure, and there is serious information loss when using classical measurement methods to construct its data structure. Thus, the promotion of GCN is hindered in information systems. This paper applies GCN to the classification of information systems. Firstly, an intuitionistic fuzzy relation, called positive-negative relation, is established in the information system, and the physical meaning of this relationship is explained. Secondly, based on the positive-negative relation, a positive-negative relation graph convolution neural network model is constructed called PN-GCN. Finally, the effectiveness and robustness of PN-GCN are validated through experiments.
Mineral prospectivity mapping using attention-based convolutional neural network

Data-driven mineral prospectivity mapping (MPM) based on deep learning methods has become a powerful tool for mineral exploration targeting in the past years. Convolutional neural networks (CNNs) have shown great success in this field because of their powerful ability to capture the complex spatial geo-anomalies related to mineralization. However, the exploration big data applied to MPM mainly relies on the high dimensions of evidence layers (other than spatial dimensions), namely, a large number of channels. This impedes the extraction of key channel features related to mineralization when using traditional CNNs. In this paper, we developed an ensemble MPM method based on CNN and Attention model: the ATT–CNN method. Specifically, a channel attention layer is added after the convolution operation of the CNN to enhance the extraction of key channel features in complex exploration data, thereby improving the feature extraction ability and prediction accuracy of CNN for MPM. A case study of W–Sn mineral prospectivity modeling in the Nanling metallogenic belt in South China was used to verify the proposed method. To alleviate the issue of training sample scarcity, we used data augmentation methods (including sliding window and random zero noise addition) when training CNN models. The results show that the prediction accuracies of the ATT–CNN model (92.949% and 94.872% using sliding window and random zero noise addition, respectively) are higher than those of the traditional CNN (91.667% and 92.308%, respectively). Moreover, the improved areas under the receiver operating characteristic curves (AUC) of ATT–CNN (0.987 and 0.971) compared to those of the CNN (0.970 and 0.964) suggest that the proposed ensemble method improves the geological generalization of CNN. The high agreement with known deposits suggests that the areas targeted in this study can guide future mineral exploration of the W–Sn mineralization in the Nanling range.
Semantic Information Market For The Metaverse: An Auction Based Approach

In this paper, we address the networking and communications problems of creating a digital copy in the Metaverse digital twin. Specifically, a virtual service provider (VSP) which is responsible for creating and rendering the Metaverse, is required to use the data collected by IoT devices to create the virtual copy of the physical world. However, due to the huge volume of the collected data by IoT devices (e.g., images and videos) and the limited bandwidth, the VSP might become unable to retrieve all the required data from the physical world. Furthermore, the Metaverse needs fast replication (e.g., rendering) of the digital copy adding more restrictions on the data transmission delay. To solve the aforementioned challenges, we propose to equip the IoT devices with semantic information extraction algorithms to minimize the size of the transmitted data over the wireless channels. Since many IoT devices will be interested to sell their semantic information to the VSP, we propose a truthful reverse auction mechanism that helps the VSP select only IoT devices that can improve the quality of its virtual copy of objects through the semantic information. We conduct extensive simulations on a dataset that contains synchronized camera and radar images, and show that our novel design enables a fast replication of the digital copy with high accuracy.
Dual-YOLO Architecture from Infrared and Visible Images for Object Detection

With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.
The Impact of Blockchain on the Quality of Accounting Information: An Iraqi Case Study

This paper aims to investigate the impact of blockchain on the quality of the information in listed and non-listed companies in Iraq; the temporal scope of this study is 2022. The statistical population of this research is divided into two parts: one part is related to the level of familiarity with blockchain technology of accountants, independent auditors, managers, etc., and the other part is related to the effect of blockchain technology on the quality of accounting information. The sample size is determined based on Cochran’s formula, among which 1528 respondents were selected as a sample size. The results of the hypothesis testing showed that in both listed and non-listed companies, familiarity with blockchain technology had increased the quality of information. In this way, blockchain technology has positively and significantly impacted the quality of accounting information. This means that the impact of IT (Blockchain) on the quality of accounting information is the same for Iraqi listed and non-listed companies. Since the current research has been investigated in an emerging market such as Iraq, it can bring helpful information to readers in this field.
Online payment fraud: from anomaly detection to risk management

Abstract Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective.
An adaptive differential evolution algorithm with population size reduction strategy for unconstrained optimization problem

The differential evolution (DE) algorithm is a heuristic random search algorithm that optimizes the problem based on population evolution. It has been widely studied for its advantages of fewer control parameters, fast convergence speed, and strong robustness. Here, an Adaptive Guided Differential Evolution algorithm on Mutation, Parameter and Population (AGDE-MPP) is proposed, which has improved the DE algorithm by adopting a new mutation scheme, a parameter adaptation scheme, and a non-linear population size reduction strategy. The new mutation scheme uses two ordered difference vectors to perturb the mutation direction of the base vector. The parameter adaptation scheme generates the mutation factor by the Cauchy distribution with the mean of the Lehmer mean value from the historical successful mutation factor pool. The new non-linear population size reduction strategy adopts a hyperbolic tangent function curve to control the population size. This adaptive algorithm can balance the exploration and exploitation capabilities at different stages of evolution and obtain solutions with high accuracy and fast convergence speed. The performance of AGDE-MPP and several state-of-the-art algorithms were evaluated on CEC2013 and CEC2017 test suites for 10, 30, and 50 dimensions. The experimental results show that the performance of AGDE-MPP is superior to almost compared algorithm and has strong competitiveness.
A Serverless Computing Fabric for Edge &amp; Cloud

Serverless computing has been establishing itself as a compelling paradigm for the development and of modern cloud-native applications. Serverless represents the next step in the evolution of cloud programming models, services and platforms, which is especially appealing due to its low management overhead, easy deployment, scale-to-zero and the promise of optimized costs. Recently, due to the advantages it offers, the serverless paradigm has been growing beyond traditional clouds, making its way to the Edge. The natural evolutionary step for serverless computing is to unify the Edge and the Cloud into what we refer to as Edge-Cloud Continuum. In this paper, we outline our vision of the Serverless Computing Fabric (SCF) for the Edge-Cloud continuum. We introduce the reference architecture for the SCF and show how it unlocks the full potential of the Edge-Cloud continuum. We also discuss main opportunities and challenges, which need to be overcome in order to achieve the vision of the Serverless Computing Fabric. Finally, we introduce key design principles together with core enabling runtime mechanisms, which are intended to serve as a research road map towards the Serverless Computing Fabric for Edge-Cloud continuum.