Reviewerly/exHarmony huggingface dataset:
From ChatGPT to CatGPT

This paper explores the potential of language models such as ChatGPT to transform library cataloging. Through experiments with ChatGPT, the author demonstrates its ability to generate accurate MARC records using RDA and other standards such as the Dublin Core Metadata Element Set. These results demonstrate the potential of ChatGPT as a tool for streamlining the record creation process and improving efficiency in library settings. The use of AI-generated records, however, also raises important questions related to intellectual property rights and bias. The paper reviews recent studies on AI in libraries and concludes that further research and development of this innovative technology is necessary to ensure its responsible implementation in the field of library cataloging.
Anonymous, Secure, Traceable, and Efficient Decentralized Digital Forensics

Digital forensics is crucial to fight crimes around the world. Decentralized Digital Forensics (DDF) promotes it to another level by channeling the power of blockchain into digital investigations. In this work, we focus on the privacy and security of DDF. Our motivations arise from (1) how to track an anonymous-and-malicious data user who leaks only a part of the previously requested data, (2) how to achieve access control while protecting data from untrusted data centers, and (3) how to enable efficient and secure search on the blockchain. To address these issues, we propose Themis: an anonymous and secure DDF scheme with traceable anonymity, private access control, and efficient search. Our framework is boosted by establishing a Trusted Execution Environment in each authority (blockchain node) for securing the uploading, requesting, and searching. To instantiate the framework, we design a secure and robust watermarking scheme in conjunction with decentralized anonymous authentication, a private and fine-grained access control scheme, and an efficient and secure search scheme based on a dynamically updated data structure. We formally define and prove the privacy and security of Themis. We build a prototype with Ethereum and Intel SGX2 to evaluate its performance, which supports processing data from a considerable number of data providers and investigators.
An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images

Unmanned aerial vehicles (UAVs), renowned for their rapid deployment, extensive data collection, and high spatial resolution, are crucial in locating distressed individuals during search and rescue (SAR) operations. Challenges in maritime search and rescue include missed detections due to issues including sunlight reflection. In this study, we proposed an enhanced ABT-YOLOv7 algorithm for underwater person detection. This algorithm integrates an asymptotic feature pyramid network (AFPN) to preserve the target feature information. The BiFormer module enhances the model’s perception of small-scale targets, whereas the task-specific context decoupling (TSCODE) mechanism effectively resolves conflicts between localization and classification. Using quantitative experiments on a curated dataset, our model outperformed methods such as YOLOv3, YOLOv4, YOLOv5, YOLOv8, Faster R-CNN, Cascade R-CNN, and FCOS. Compared with YOLOv7, our approach enhances the mean average precision (mAP) from 87.1% to 91.6%. Therefore, our approach reduces the sensitivity of the detection model to low-lighting conditions and sunlight reflection, thus demonstrating enhanced robustness. These innovations have driven advancements in UAV technology within the maritime search and rescue domains.
Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design

Two-dimensional (2D) semiconductors are central to many scientific fields. The combination of two semiconductors (heterostructure) is a good way to lift many technological deadlocks. Although ab initio calculations are useful to study physical properties of these composites, their application is limited to few heterostructure samples. Herein, we use machine learning to predict key characteristics of 2D materials to select relevant candidates for heterostructure building. First, a label space is created with engineered labels relating to atomic charge and ion spatial distribution. Then, a meta-estimator is designed to predict label values of heterostructure samples having a defined band alignment (descriptor). To this end, independently trained k-nearest neighbors (KNN) regression models are combined to boost the regression. Then, swarm intelligence principles are used, along with the boosted estimator’s results, to further refine the regression. This new “swarm smart” algorithm is a powerful and versatile tool to select, among experimentally existing, computationally studied, and not yet discovered van der Waals heterostructures, the most likely candidate materials to face the scientific challenges ahead.
How to promote consumption in city metaverse? Research on RAR experience design and consumer behavior of commercial streets

The emergence of innovative technologies such as 5G, big data, and XR immersive technology is providing richer data sets and a deeper understanding of human behavior, which has the potential to redefine urban design activities and service provision through the metaverse. In the current wave of metaverse digital planning, commercial streets are a significant aspect of urban cultural and tourism, but there is limited knowledge on how to integrate metaverse digital content in real urban spaces to promote consumption.This study aims to investigate the factors that promote purchase in RAR (Reality + augmented reality) environments by integrating the concept of city metaverse and related work on XR technologies in consumer experience and behavior. We have constructed a model of RAR-induced consumer behavior by modifying the SOR theoretical model with our project experience. Our research identifies three external drivers of RAR: RAR features, RAR multidimensional experience, and RAR marketing. These external stimuli produce changes in RAR consumer perceptions in three areas: hedonic, utilitarian, and social value. Changes in consumer perceptions can lead to subsequent consumer behavior, such as enhanced willingness to consume, willingness to recommend, and loyalty. These findings have broader applications in the context of the city metaverse, providing designers and researchers with new insights into the technology of virtual-real integration and inspiration for design patterns. Additionally, our study provides commercial space operators with reference value for RAR marketing guidelines.
Performance Analysis of GANs for De-Noising Images

This report discusses the problem of denoising in image processing and the application of Generative Adversarial Networks (GANs) to address this challenge. GANs have demonstrated promising results in denoising tasks by learning to generate clean images from noisy ones through training on paired noisy and clean image datasets. Several variations of GANs have been proposed for denoising, including SRGAN, DCGAN, and LSGAN, each with unique strengths and weaknesses. This report suggests conducting a comparative study to determine the best-performing model under different conditions. The comparative study involves evaluating the denoising performance of these GAN models on a shared dataset, using metrics such as Peak Signal-to-Noise Ratio (PSNR). Through this paper we want to present you a comparative study on denoising images using SRGAN, DCGAN, Auto encoding GAN, Vanila GAN, and LSGAN to provide insights into the strengths and limitations of each model and the main aim of this study is to provide a guide to the best way to denoise the images.
Advanced Learning Schemes for Metaverse Applications in B5G/6G Networks

The metaverse, a virtual world where various users can jointly/independently interact with digital objects seamlessly, is expected to be a key application area in beyond fifth generation (B5G)/ six-generation (6G) networks. However, the metaverse design poses several challenges, such as efficient usage of limited resources and the provision of high-quality user experiences. In this paper, we recommend advanced machine learning techniques such as multi-armed bandits (MABs), federated learning (FL), meta-learning, etc., to address these challenges. We illustrate the evaluability of these techniques with two specific use cases: resource allocation for virtual reality (VR) applications and personalized content delivery in the metaverse.
Redactable Blockchain-Assisted Secure Data Aggregation Scheme for Fog-Enabled Internet-of-Farming-Things

Internet-of-Farming Things (IoFT)-enabled smart agriculture can collect data more reliably and frequently to track the crop’s status and other significant information. Considering that smart agriculture requires working with substantial amounts of sensitive data. In light of this, frequent data processing may threaten the confidentiality and integrity of data and IoFT device privacy. Although numerous privacy-preserving data aggregation methods have been implemented to address these issues, they also have certain security vulnerabilities, such as inadequate data confidentiality, collusion attacks, and malicious data mining attacks. Therefore, we introduce a three-tier architecture-assisted redactable blockchain-based secure data aggregation method with source authentication for the fog-enabled IoFT1. This work provides an efficient and secure two-level data aggregation model. The proposed model supports resistance to collusion and malicious data mining threats launched by internal or external attackers. It can also achieve perfect data confidentiality and integrity against a malicious aggregator and an inquisitive control center for an authorized IoFT device. Specifically, the detailed performance analysis and theoretical concrete security proofs demonstrate the practicability and efficiency of the proposed model.
Grey Wolf Optimization for Facial Emotion Recognition: Survey

Human face expression Recognition is one of the most effective forms of social communication. Generally, facial expressions are a simple and obvious way for people to express their feelings and intentions. Typically, the goal of facial expression recognition is to categorize facial expressions into specific classes of expression labels. This paper presents a survey of facial emotion expression classification based on different machine learning and deep learning mechanisms and optimization algorithms. In order to evaluate the basic emotion of a person's face, a technology called facial expression recognition employs a computer as a helper with specific algorithms. Seven basic emotions were represented by facial expressions, including a smile, sadness, anger, disgust, surprise, fear, and a natural expression. In this paper, the focus is on using the Grey Wolf algorithm for selection of the optimal features from feature extraction from input image faces to recognize human facial emotions. In most studies, the FER system was applied to popular datasets such as the JAFEE database and the Cohn-Kanade database.
Double a-g-Transformation and Its Properties

In this paper, we introduce a new transformation , namely , a-g-transformation and denote it as . This transformation consider as generalized to double g-transformation. We defined it as the following form :&#x0D;
First Chebyshev Wavelet in Numerical Solution and Signal Processing

In this work, a kind of wavelet used in many mathematical problems and numerically solved, such as heterogeneity and integral equations, are presented. The solution is close to the exact solution using the matrix of integral operations. In this paper, the issues of limited value were solved and good solutions were found. Solved examples show that.In addition, the proposed theory was used to process signals for one dimension and the noise and signal pressure were removed this was applied to some kind of signal.
Learning Wasserstein Contrastive Color Histogram Representation for Low-Light Image Enhancement

The goal of low-light image enhancement (LLIE) is to enhance perception to restore normal-light images. The primary emphasis of earlier LLIE methods was on enhancing the illumination while paying less attention to the color distortions and noise in the dark. In comparison to the ground truth, the restored images frequently exhibit inconsistent color and residual noise. To this end, this paper introduces a Wasserstein contrastive regularization method (WCR) for LLIE. The WCR regularizes the color histogram (CH) representation of the restored image to keep its color consistency while removing noise. Specifically, the WCR contains two novel designs including a differentiable CH module (DCHM) and a WCR loss. The DCHM serves as a modular component that can be easily integrated into the network to enable end-to-end learning of the image CH. Afterwards, to ensure color consistency, we utilize the Wasserstein distance (WD) to quantify the resemblance of the learnable CHs between the restored image and the normal-light image. Then, the regularized WD is used to construct the WCR loss, which is a triplet loss and takes the normal-light images as positive samples, the low-light images as negative samples, and the restored images as anchor samples. The WCR loss pulls the anchor samples closer to the positive samples and simultaneously pushes them away from the negative samples so as to help the anchors remove the noise in the dark. Notably, the proposed WCR method was only used for training, and was shown to achieve high performance and high speed inference using lightweight networks. Therefore, it is valuable for real-time applications such as night automatic driving and night reversing image enhancement. Extensive evaluations on benchmark datasets such as LOL, FiveK, and UIEB showed that the proposed WCR method achieves superior performance, outperforming existing state-of-the-art methods.
Constructional Approaches to Nordic Languages

This volume presents eight studies of linguistic phenomena in Nordic languages (notably Danish, Norwegian, and Swedish) from a construction grammar perspective. The contributions both deepen and widen the focus of construction grammar applied to Nordic languages by dealing with a variety of topics, such as the constructional network, pseudo-coordination, additional language learning and emerging multilingualism, prototypical semantics in argument structure constructions, and domain-specific discourse and language behavior. The volume showcases the vibrant research activity within part of the construction grammar community dealing with Nordic languages, contributing to the knowledge about the structure, use and learning of these languages, as well as to the field of construction grammar as a whole.
Theoretical Framework and Practical Considerations for Achieving Superior Multi-Robot Exploration: Hybrid Cheetah Optimization with Intelligent Initial Configurations

Efficient exploration in multi-robot systems is significantly influenced by the initial start positions of the robots. This paper introduces the hybrid cheetah exploration technique with intelligent initial configuration (HCETIIC), a novel strategy explicitly designed to optimize exploration efficiency across varying initial start configurations: uniform distribution, centralized position, random positions, perimeter positions, clustered positions, and strategic positions. To establish the effectiveness of HCETIIC, we engage in a comparative analysis with four other prevalent hybrid methods in the domain. These methods amalgamate the principles of coordinated multi-robot exploration (CME) with different metaheuristic algorithms and have demonstrated compelling results in their respective studies. The performance comparison is based on essential measures such as runtime, the percentage of the explored area, and failure rate. The empirical results reveal that the proposed HCETIIC method consistently outperforms the compared strategies across different start positions, thereby emphasizing its considerable potential for enhancing efficiency in multi-robot exploration tasks across a wide range of real-world scenarios. This research underscores the critical, yet often overlooked, role of the initial robot configuration in multi-robot exploration, establishing a new direction for further improvements in this field.
The Dynamic Connectedness between Cryptocurrencies and Foreign Exchange Rates: Evidence by TVP-VAR Approach

We investigate the interconnectedness between major cryptocurrencies and foreign exchange rates. This study employs time-series daily data for the cryptocurrencies and foreign exchange rates closing prices, the data is obtained from investing.com and yahoo finance to cover the period of 10 November 2017 to 18 January 2022. The study adopts the connectedness approach developed by Diebold Yilmaz (2014), using the TVP-VAR model to analyze twelve cryptocurrencies and eight foreign exchange rates. The results reveal a greater degree of connectedness across cryptocurrencies and foreign exchange rates over the whole sample, pre and during the corona pandemic, indicating that the Corona pandemic donates to the increase of volatility spillover across the currency and cryptocurrency markets. The results further show that Ethereum, Bitcoin Cash, Litecoin, Bitcoin, TRON, Cardano and Ripple are the main transmitters of shocks to other cryptocurrencies. Moreover, the EUR/USD, AUD/USD and NZD/USD are the main transmitters of shocks to other foreign exchange rates. The study has significant implications for investors, and portfolio managers. Our results offer evidence to improve financial risk assessment, and portfolio hedging strategies of cryptocurrencies against the uncertainty raised by Covid-19 Pandemic, which our findings may support investors in properly rebalancing their portfolios as the level of uncertainty in the market changes.
A new method for safety helmet detection based on convolutional neural network

Considering practical issues such as cost control of hardware facilities in engineering projects, it is a challenge to design a robust safety helmet detection method, which can be implemented on mobile or embedded devices with limited computing power. This paper presents an approach to optimize the BottleneckCSP structure in the YOLOv5 backbone network, which can greatly reduce the complexity of the model without changing the size of the network input and output. To eliminate the information loss caused by upsampling and enhance the semantic information of the feature map on the reverse path, this paper designs an upsampling feature enhancement module. Besides, To avoid the negative impact of redundant information generated by feature fusion on the detection results, this paper introduces a self-attention mechanism. That is, using the designed channel attention module and location attention module, adjacent shallow feature maps and upsampled feature maps are adaptively fused to generate new feature maps with strong semantics and precise location information. Compared with the existing methods with the fastest inference speed, under the same compute capability, the proposed method not only has faster inference speed, the FPS can reach 416, but also has better performance with mAP of 94.2%.
Implementing the Teaching at the Right Level (TaRL) Approach to Improve Elementary Students' Initial Reading Skills

This research was motivated by students' low initial reading skills in Class I MIN 1 Mataram City. Of the 130 students, 38 students could not read at the initial. This research aims to improve students' initial reading skills by implementing the Teaching at the Right Level (TaRL) Approach Assisted by Inovasi Materials in Class I MIN 1 Mataram City. This type of research is classroom action research (CAR). The research setting is at MIN 1 Mataram City. The study aims to improve students' initial reading skills through the TaRL Approach. The research was carried out in two cycles, which included four stages: planning, implementing and observing, evaluating, and reflecting. The research instruments used were interviews, observations, tests, and documentation. Data analysis was carried out qualitatively and quantitatively. The success of this research is if student activity is at least in the active category and students' initial reading ability is at least 85% of students get a score of 70. The research results show that implementing the Teaching at the Right Level (TaRL) approach assisted by Inovasi Materials can improve students' initial reading ability in Class I MIN 1 City of Mataram. This can be seen from the evaluation results that can be seen from the average value of Cycle I, 66 to 81.36 in Cycle II. Then, the number of students who completed was seen from 20 students in Cycle I to 33 students in Cycle II, or it could be interpreted that the percentage in Cycle I was 52.63%, increasing to 86.84% in Cycle II.
Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury

Abstract Drug-induced liver injury (DILI) presents significant diagnostic challenges, and recently artificial intelligence-based deep learning technology has been used to predict various hepatic findings. In this study, we trained a set of Mask R-CNN-based deep algorithms to learn and quantify typical toxicant induced-histopathological lesions, portal area, and connective tissue in Sprague Dawley rats. We compared a set of single-finding models (SFMs) and a combined multiple-finding model (MFM) for their ability to simultaneously detect, classify, and quantify multiple hepatic findings on rat liver slide images. All of the SFMs yielded mean average precision (mAP) values above 85%, suggesting that the models had been successfully established. The MFM showed better performance than the SFMs, with a total mAP value of 92.46%. We compared the model predictions for slide images with ground-truth annotations generated by an accredited pathologist. For the MFM, the overall and individual finding predictions were highly correlated with the annotated areas, with R-squared values of 0.852, 0.952, 0.999, 0.990, and 0.958 being obtained for portal area, infiltration, necrosis, vacuolation, and connective tissue (including fibrosis), respectively. Our results indicate that the proposed MFM could be a useful tool for detecting and predicting multiple hepatic findings in basic non-clinical study settings.