Reviewerly/exHarmony huggingface dataset:
Zero‐trust‐based security model against data breaches in the banking sector: A blockchain consensus algorithm

Abstract Cyber security in the banking sector is of high importance nowadays. The rate of cyberattacks is spiking every year, and the implementation of strong cybersecurity models is required to ensure the confidentiality and integrity of data. Since protecting a bank requires a wide range of security practices, this paper focuses on protecting the bank resources from malicious actors and securing the transactions using a blockchain consensus mechanism that uses a zero‐trust security approach among the participants in the transaction. In addition to the framework, an algorithm for blockchain‐based online transactions was designed to make use of practical implementation in the future. The ideas formulated during the research and literature review were integrated to design the framework and the algorithm. The proposed framework ensures that the security of the banking sector can be enhanced by adopting the zero‐trust concept and blockchain technology. The consensus algorithms used for the transaction make it immutable and decentralized. Zero‐trust principles adopted in the model ensure the confidentiality and integrity of the banking system.
Poseidon: Practical Homomorphic Encryption Accelerator

With the development of the important solution for privacy computing, the explosion of data size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous challenges to the hardware design. In this paper, we propose a practical FHE accelerator - "Poseidon", which focuses on improving the hardware resource and bandwidth consumption. Poseidon supports complex FHE operations like Bootstrapping, Keyswitch, Rotation and so on, under limited FPGA resources. It refines these operations by abstracting five key operators: Modular Addition (MA), Modular Multiplication (MM), Number Theoretic Transformation (NTT), Automorphsim and Shared Barret Reduction (SBT). These operators are combined and reused to implement higher-level FHE operations. To utilize the FPGA resources more efficiently and improve the parallelism, we adopt the radix-based NTT algorithm and propose HFAuto, an optimized automorphism implementation suitable for FPGA. Then, we design the hardware accelerator based on the optimized key operators and HBM to maximize computational efficiency. We evaluate Poseidon with four domain-specific FHE benchmarks on Xilinx Alveo U280 FPGA. Empirical results show that the efficient reuse of the operator cores and on-chip storage enables superior performance compared with the state-of-the-art GPU, FPGA and accelerator ASICs. We highlight the following results: (1) up to 370× speedup over CPU for the basic operations of FHE; (2) up to 1300×/52× speedup over CPU and the FPGA solution for the key operators; (3) up to 10.6×/8.7× speedup over GPU and the ASIC solution for the FHE benchmark.
Miro: Promoting Collaboration through Online Whiteboard Interaction

To create a more collaborative and communicative learning environment, digital tools are often looked to as possible solutions. Miro, an online whiteboard, is one such tool that allows students to easily share ideas and exchange information using a range of different media. Its strengths are its ability to allow real-time sharing and creation of ideas and information between learners and teachers using a host of multimedia. However, the numerous tools and options provided by the platform does come with a learning curve. Nevertheless, Miro is a useful option for encouraging collaboration between students, giving them a workspace to freely generate ideas and share resources.
Recent advances of Transformers in medical image analysis: A comprehensive review

Abstract Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario‐specific architectural variations. In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer‐based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up‐to‐date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.
Understanding blockchain applications in Industry 4.0: From information technology to manufacturing and operations management

The current literature regarding blockchain-based applications in the context of Industry 4.0 has rapidly grown during the last decade. However, a systematic literature review that summarizes the main contributions, findings, and implications from a managerial perspective of the blockchain technology adoption in the specific context of Industry 4.0 is still missing. The present article aims to fill this research gap by examining and elaborating on the extant literature to develop a literature-grounded framework (WHY-HOW-WHAT) that helps better understand the management issues that blockchain technology can help resolve in the context of Industry 4.0, as well as identify the main features of blockchain-based solutions in various areas of Industry 4.0. Furthermore, the proposed framework is useful to understand how ten Industry 4.0 enabling technologies combine with the blockchain technology to implement efficient and effective blockchain-based solutions in Industry 4.0 settings. Finally, based on this framework we conjecture the trajectories of the evolution of blockchain technology in Industry 4.0 settings, and highlight the relevant research gaps that both academics and practitioners working on this field should address in the near future.
UAV High-Voltage Power Transmission Line Autonomous Correction Inspection System Based on Object Detection

With the development of technology, unmanned aerial vehicles (UAVs) are playing an increasingly important role in the inspection of high-voltage power transmission line. The traditional inspection method relies on the operator to manually control the drone for inspection. Although many companies are using real-time dynamic carrier phase differencing technology to achieve high-precision positioning of UAVs, when UAVs fly autonomously at high altitudes to photograph specific objects, the objects tend to deviate from the center of the picture. To address this error, in this article, an autonomous UAV inspection system based on object detection is designed: 1) to detect inspection objects, the corresponding dataset is established on the basis of the UAV autonomous inspection task; 2) to obtain the position information of the target object, a lightweight object detector based on the YOLOX network model is designed. First, the backbone is replaced with MobileNetv3. Next, in the neck structure, the Ghost module is introduced and depthwise convolution is applied instead of normal convolution. Then, to embed the location information into the channel attention, coordinate attention (CA) is introduced after the output feature layer of the backbone, enabling the lightweight network to operate on a larger area of focus. Finally, to improve the accuracy of the bounding box regression, the <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> -distance-IoU (DIOU) loss function is introduced; 3) to obtain the best image acquisition position, the results of object detection combined with the real-time status of the UAV are used; and 4) to enable the UAV to complete the final corrections, position control and altitude control are used. Compared with the original YOLOX_tiny, the new model improves the mAP_0.5:0.95 metric by about 2% points, with a significant reduction in the number of parameters and computation, while running at 56 frames/s on Nvidia NX. This system can effectively solve the problem of the target deviating from the center of the picture when the UAV takes pictures during a high-altitude autonomous inspection, verified by many actual flight experiments.
Voice artificial intelligence service failure and customer complaint behavior: The mediation effect of customer emotion

According to the literature, customer service failure and negative customer reactions are closely associated. However, there has been little exploration of the relationship between service failure and negative customer reactions in the context of artificial intelligence (AI) services. Based on a conceptual model, this study explores the impact of voice AI service failure on customer complaint behavior. A proprietary dataset from a telecommunications firm was used to evaluate the utility of the conceptual model. Customer emotion was tested to mediate between voice AI service failure and customer complaint behavior. We found that the failure of voice AI services substantially influences customer complaint behavior. AI service failure increases the likelihood of complaints to a customer in the call center. Furthermore, customer emotion plays an essential mediating role. Our findings have crucial management implications for customer relationship management and recovery service.
Augmented reality in marketing: Conceptualization and systematic review

Abstract This systematic review of the literature focuses on the use of AR and its impacts in the marketing area. It provides a multidisciplinary, up‐to‐date synthesis of the literature and an exhaustive classification of AR. Through the use of the SPAR‐4‐SLR protocol, 148 articles were selected for analysis. The study has three main objectives. First, it reports the key characteristics (distribution by year, publication outlets, etc.), theoretical models, and methodologies used in this research domain. Second, it suggests a classification of the types of AR according to their triggers and the object of the augmentation (self vs. external). Third, it proposes a framework that presents (1) the AR features and attributes and the AR use experience; (2) the cognitive, affective, and social mediators; and (3) the outcomes of these experiences. Key moderators (types of AR, types of products, individual characteristics, etc.) are also discussed. Using the TCCM framework (theories, context, characteristics, and methodologies), this study offers several future research avenues and highlights the importance of considering the effects of the different types of AR. Finally, it offers pointers for managers on how to develop efficient AR solutions and how these can be used to reduce a company's carbon footprint.
A Novel MPPT Controller Based on Mud Ring Optimization Algorithm for Centralized Thermoelectric Generator under Dynamic Thermal Gradients

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions.
U-Net Architecture for Prostate Segmentation: The Impact of Loss Function on System Performance

Segmentation of the prostate gland from magnetic resonance images is rapidly becoming a standard of care in prostate cancer radiotherapy treatment planning. Automating this process has the potential to improve accuracy and efficiency. However, the performance and accuracy of deep learning models varies depending on the design and optimal tuning of the hyper-parameters. In this study, we examine the effect of loss functions on the performance of deep-learning-based prostate segmentation models. A U-Net model for prostate segmentation using T2-weighted images from a local dataset was trained and performance compared when using nine different loss functions, including: Binary Cross-Entropy (BCE), Intersection over Union (IoU), Dice, BCE and Dice (BCE + Dice), weighted BCE and Dice (W (BCE + Dice)), Focal, Tversky, Focal Tversky, and Surface loss functions. Model outputs were compared using several metrics on a five-fold cross-validation set. Ranking of model performance was found to be dependent on the metric used to measure performance, but in general, W (BCE + Dice) and Focal Tversky performed well for all metrics (whole gland Dice similarity coefficient (DSC): 0.71 and 0.74; 95HD: 6.66 and 7.42; Ravid 0.05 and 0.18, respectively) and Surface loss generally ranked lowest (DSC: 0.40; 95HD: 13.64; Ravid -0.09). When comparing the performance of the models for the mid-gland, apex, and base parts of the prostate gland, the models' performance was lower for the apex and base compared to the mid-gland. In conclusion, we have demonstrated that the performance of a deep learning model for prostate segmentation can be affected by choice of loss function. For prostate segmentation, it would appear that compound loss functions generally outperform singles loss functions such as Surface loss.
Gaussian Mutation Specular Reflection Learning with Local Escaping Operator Based Artificial Electric Field Algorithm and Its Engineering Application

During the contribution of a metaheuristic algorithm for solving complex problems, one of the major challenges is to obtain the one that provides a well-balanced exploration and exploitation. Among the possible solutions to overcome this issue is to combine the strengths of the different methods. In this study, one of the recently developed metaheuristic algorithms, artificial electric field algorithm (AEFA), has been used, to improve its converge speed and the ability to avoid the local optimum points of the given problems. To address these issues, Gaussian mutation specular reflection learning (GS) and local escaping operator (LEO) have been added to the essential steps on AEFA and called GSLEO-AEFA. In order to observe the effect of the applied features, 23 benchmark functions as well as engineering and real-world application problems were tested and compared with the other algorithms. Friedman and Wilcoxon rank-sum statistical tests, and complexity analyses were also conducted to measure the performance of GSLEO-AEFA. The overall effectiveness of the algorithm among the compared algorithms obtained in between 84.62–92.31%. According to the achieved results, it can be seen that GSLEO-AEFA has precise optimization accuracy even in changing dimensions, especially in engineering optimization problems.
Blockchain and Interplanetary File System (IPFS)-Based Data Storage System for Vehicular Networks with Keyword Search Capability

Closed-circuit television (CCTV) cameras and black boxes are indispensable for road safety and accident management. Visible highway surveillance cameras can promote safe driving habits while discouraging moving violations. According to CCTV laws, footage captured by roadside cameras must be securely stored, and authorized persons can access it. Footages collected by CCTV and Blackbox are usually saved to the camera’s microSD card, the cloud, or hard drives locally but there are concerns about security and data integrity. These issues may be addressed by blockchain technology. The cost of storing data on the blockchain, on the other hand, is prohibitively expensive. We can have decentralized and cost-effective storage with the interplanetary file system (IPFS) project. It is a file-sharing protocol that stores and distributes data in a distributed file system. We propose a decentralized IPFS and blockchain-based application for distributed file storage. It is possible to upload various types of files into our decentralized application (DApp), and hashes of the uploaded files are permanently saved on the Ethereum blockchain with the help of smart contracts. Because it cannot be removed, it is immutable. By clicking on the file description, we can also view the file. DApp also includes a keyword search feature to assist us in quickly locating sensitive information. We used Ethers.js’ smart contract event listener and contract.queryFilter to filter and read data from the blockchain. The smart contract events are then written to a text file for our DApp’s keyword search functionality. Our experiment demonstrates that our DApp is resilient to system failure while preserving the transparency and integrity of data due to the immutability of blockchain.
HiSup: Accurate polygonal mapping of buildings in satellite imagery with hierarchical supervision

This paper studies the problem of the polygonal mapping of buildings by tackling the issue of mask reversibility, which leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments, and high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on four public benchmarks, including the AICrowd, Open Cities, Shanghai, and Inria datasets. On the AICrowd, Open Cities, and Shanghai datasets, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS by large margins. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU/HiSup.
Applications and Possible Challenges of Healthcare Metaverse

A Metaverse is a world in which users can virtually interact with each other. It is a mixture of both augmented reality (AR) and virtual reality (VR), where users can perform different activities that give them a feeling of the real world. Overall, the metaverse is redefining the mode of our communication. The rapid modernization of technology has transformed all industries in the market, including the healthcare sector. After the announcement of the metaverse by the CEO of META, Mark Zuckerberg, the metaverse has received got massive attention from the medical industry. Overall, the metaverse is considered a huge game changer in the medical industry in 2022 and 2023. With productive opportunities in the medical industry, metaverse technology is one of the most profitable and promising technologies. Metaverse is a modern technology that can address multiple issues in the healthcare industry, including virtual health and fitness, mental health, health access without geographical limitations, and connection with friends and family members. In this study, we summarize the use of the metaverse and its future directions in the healthcare industry. We also elaborate on the main challenges that might be faced when using metaverse technology in the future.
Improved blood cell detection method based on YOLOv5 algorithm

The proposed blood cell target detection algorithm based on YOLOv5 addresses the issue of low average accuracy and serious miss detection due to small blood cells and serious cell adhesion in blood cell detection by target detection algorithms. By adding the CBAM (Convolutional Block Attention Module) to the YOLOv5 framework's backbone network and the BIFPN (bidirectional feature pyramid network) to the neck network, the algorithm improves the model's ability to extract features. The experimental results show that the average accuracy (mAP) of the improved YOLOv5 blood cell target detection algorithm is 89.9%, representing a increase over the native YOLOv5s type, and the recall rate and accuracy rate are also increased by 3.2% and 4.2%, respectively. This meets the requirements of the actual scene for blood cell detection.
Exemplar-free class incremental learning via discriminative and comparable parallel one-class classifiers

The exemplar-free class incremental learning (IL) requires classification models to learn new-class knowledge incrementally without retaining any old samples. Recently, the IL framework based on parallel one-class classifiers (POC) has demonstrated promising performance. It trains a one-class classifier (OCC) for each category and thus is immune to the catastrophic forgetting problem. However, the single-class training strategy may incur weak discriminability and low comparability between different classifiers in POC. To meet this challenge, we propose a new IL framework, referred to as Discriminative and Comparable Parallel One-class Classifiers (DCPOC). Instead of ordinary OCCs (e.g., deep SVDD) used in other POC methods, DCPOC adopts variational auto-encoders (VAE) as OCCs because VAEs can be used not only to identify classes for given samples but also to generate pseudo samples for the trained classes. With this advantage, DCPOC trains a new-class VAE in contrast with the old-class VAEs, which benefits the new-class VAE to reconstruct better for new-class samples but worse for old-class pseudo samples, thus enhancing the comparability. Furthermore, DCPOC introduces a hinge reconstruction loss to reinforce the discriminability. We evaluate our method on MNIST, CIFAR10, CIFAR100, Tiny-ImageNet, and ImageNet. The experimental results show that DCPOC achieves state-of-the-art performance on these datasets.1