Reviewerly/exHarmony huggingface dataset:
Fake news detection using recurrent neural network based on bidirectional LSTM and GloVe

In the world of technology, the electronic and technical development of the fields of communication and the internet has increased, which has caused a renaissance in the virtual world. This development has greatly impacted virtual communities for the ease and speed of communication and information transfer through social media platforms, making these platforms likable and easy to use. The social network faces major challenges due to its extensive use. As a result, many people have become involved in cybercrimes. There are accounts on the internet that are malicious. Platforms for social networking online, such as Facebook and Twitter, allow all users to freely generate and consume massive volumes of material regardless of their traits. While individuals and businesses utilize this information to gain a competitive edge, spam or phony users create important data. According to estimates, 1 in 200 posts on social media contain spam, and 1 in 21 tweets contain spam. The problem was centered around the accuracy of detecting false news and correcting it or preventing its dissemination before it spread in the network. A new method is given based on improving the false news detection system; the level of improvement was significant in the preprocessing stage where Glove is used, which is an unsupervised learning algorithm developed by researchers at Stanford University aiming to generate word embeddings by aggregating global word co-occurrence matrices from a given corpus. The basic idea behind the GloVe word embedding is to derive the relationship between the words from statistics. The proposed method contains deep learning algorithms of convolutional neural network (CNN), deep neural network (DNN), and long short-term memory (LSTM). The RNN with GloVe in the preprocessing stage using the Curpos fake news dataset to enhance the system, due to the sequential processes and classification, has the highest accuracy of 98.974%.
Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice

Abstract The emergence of artificial intelligence (AI) has transformed global business, aiding operational efficiency and innovation. It utilizes machine learning and big data analytics, driving predictive market trends and strategic decision‐making. However, despite the rising discussion and accessibility of AI tools, understanding its impact on international business remains limited. This article explores AI's potential in international business strategies, practices, and activities. To address this aim, we reviewed 37 articles in the existing literature to critically explore AI within the context of international business. More specifically, we explored how AI can be applied to innovation approaches in international business, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, managing across cultures, and more topics. AI has necessitated changes in workplace configurations and the need for organizational and employee adjustments in response to this technology. As a result of the foregoing issues on AI integration within international business, our analysis provided an exploratory discussion around its use, challenges, managerial implications, and suggested areas requiring future studies.
TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text Classification

Abstract Extreme multi-label text classification (XMTC) annotates related labels for unknown text from large-scale label sets. Transformer-based methods have become the dominant approach for solving the XMTC task due to their effective text representation capabilities. However, the existing Transformer-based methods fail to effectively exploit the correlation between labels in the XMTC task. To address this shortcoming, we propose a novel model called TLC-XML, i.e., a Transformer with label correlation for extreme multi-label text classification. TLC-XML comprises three modules: Partition, Matcher and Ranker. In the Partition module, we exploit the semantic and co-occurrence information of labels to construct the label correlation graph, and further partition the strongly correlated labels into the same cluster. In the Matcher module, we propose cluster correlation learning, which uses the graph convolutional network (GCN) to extract the correlation between clusters. We then introduce these valuable correlations into the classifier to match related clusters. In the Ranker module, we propose label interaction learning, which aggregates the raw label prediction with the information of the neighboring labels. The experimental results on benchmark datasets show that TLC-XML significantly outperforms state-of-the-art XMTC methods.
Path planning of water surface garbage cleaning robot based on improved immune particle swarm algorithm

In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path. To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest. Especially in the complex level 4 wave water environment, the AIPSO algorithm had the best adaptability and robustness, and the robot had the highest working efficiency and comprehensive performance. The experimental results revealed that the AIPSO algorithm effectively improved the path search and garbage cleaning efficiency of the robots and reduced the working time, which further verified the reliability and accuracy of the designed algorithm.
GPU Shader Analysis and Power Optimization Model

With the rapid advancements in 3D game technology, workload characterization has become crucial for each new generation of games. The increased complexity of scenes in 3D games allows for stunning real-time visual quality. However, handling such workloads results in significant power consumption over the GPU rendering pipeline. The focus of the current paper is low power optimization, targeting texture memory, geometry engine, pixel, and rasterization, as these components are significant contributors to the power consumption of a typical GPU. The proposed methodology integrates the Dynamic Voltage Frequency Scaling (DVFS) technique, adjusting voltage and frequency based on the workload analysis of frame rates with respect to the scenes of 3D games. Frame rates of 60 fps and 30 fps are set up to understand and manage the workload on frames. Furthermore, for comparative analysis, various frame-level power analysis schemes such as No DVFS implemented, Frame History Method, Frame Signature Method, and Tiled History-based are introduced. The proposed scheme consistently surpasses these frame-level schemes, with fewer missed deadlines, while having the lowest energy consumption per frame rate. The implementation resulted in a remarkable 65% improvement in quality, indicated by a reduction in deadline misses, along with a substantial 60% energy saving.
Advanced Fraud Detection in Blockchain Transactions: An Ensemble Learning and Explainable AI Approach

In recent years, cryptocurrencies have experienced rapid growth and adoption, revolutionizing the financial sector. However, the rise of digital currencies has also led to an increase in fraudulent transactions and illegal activities. In this paper, we present a comprehensive study on the detection of fraudulent transactions in the context of cryptocurrency exchanges, with a primary focus on the Ethereum network. By employing various Machine Learning (ML) techniques and ensemble methods, including the hard voting ensemble model, which achieved a remarkable 99% accuracy, we aim to effectively identify suspicious transactions while maintaining high accuracy and precision. Additionally, we delve into the importance of eXplainable Artificial Intelligence (XAI) to enhance transparency, trust, and accountability in AI-based fraud detection systems. Our research contributes to the development of reliable and interpretable models that can significantly improve the cryptocurrency ecosystem security and integrity.
The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
A Light Weight Depthwise Separable Layer Optimized CNN Architecture for Object-Based Forgery Detection in Surveillance Videos

Abstract The present era is at the peak of technological advancement in image and video processing techniques, with user-friendly accessible tools/techniques. This immersive technology development makes video forensics enormously challenging. Specifically, the passive approaches to object-based forgeries in videos are crucial for legal and judicial matters. Hence, to ensure the integrity of the videos, a scientific, statistical and passive investigation of videos is required to maintain the spatial and temporal information content. This paper aims to develop a passive approach for digging out the forgery traces by applying the motion residue windowing technique for object removal forgery in surveillance videos. The novel max averaging windowing techniques improve visual imprints of the object removal forgery in the videos from the existing methods in the literature. A deep learning approach is the next step for achieving forgery detection in surveillance videos. The proposed lightweight depth-separable layer-optimized CNN has fast execution speed, optimized in terms of parameters without compromising the desired accuracy. This network is trained at a frame level with 98.60% testing accuracy, followed by a pipeline architecture of the proposed model for detection of forgery at video level with 99.01% accuracy. The suggested model works better than current models regarding post-processing operations, compression rates, forged video detection accuracy, precision, recall and F1 score.
Enhancing higher education art students’ learning experience through virtual reality: a comprehensive literature review of product design courses

Since the turn of the millennium, virtual reality (VR) has become a popular global research topic. Educators are eager to employ VR for interactive learning, potentially enhancing student engagement and efficiency. Researchers have conducted numerous trials, with students across disciplines achieving notable results, affirming VR's effectiveness, even in art and design education. The field of product design, in particular, requires high-dimensional interaction throughout the learning process, making it a promising application area for VR technology. This study takes art and design majors as the analysis object and aims to explore the development trend of VR technology in education related to product design courses. In addition, this study conducted a systematic review of relevant literature over the past five years to assess the current state of VR technology in arts education, the types of VR used in teaching, and the impact of the technology on the student population. The results reveal that, despite the growing interest in VR technology, there have been relatively few cases of educators applying this technology to product design education. Moreover, immersive VR technology in art and design classrooms is still in the new stage. Consequently, researchers have ample scope for further exploring and expanding this field.
How to choose a proper representation of compositional data for mineral exploration?

Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale.
Lightweight Vehicle Detection Based on Improved YOLOv5s

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.
FairGAT: Fairness-aware Graph Attention Networks

Graphs can facilitate modeling various complex systems such as gene networks and power grids, as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural network-based (GNN) solutions, among which graph attention networks (GATs) have become one of the most widely utilized neural network structures for graph-based tasks. Although it is shown that the use of graph structures in learning results in the amplification of algorithmic bias, the influence of the attention design in GATs on algorithmic bias has not been investigated. Motivated by this, the present study first carries out a theoretical analysis in order to demonstrate the sources of algorithmic bias in GAT-based learning for node classification. Then, a novel algorithm, FairGAT, that leverages a fairness-aware attention design is developed based on the theoretical findings. Experimental results on real-world networks demonstrate that FairGAT improves group fairness measures while also providing comparable utility to the fairness-aware baselines for node classification and link prediction.
A novel quantum key distribution resistant against large‐pulse attacks

Abstract Quantum key distribution (QKD) offers information‐theoretic security by leveraging the principles of quantum mechanics. This means the security is independent of all future advances in algorithm or computational power. However, due to the non‐availability of single‐photon sources, most traditional QKD protocols are vulnerable to various attacks, such as photon number‐splitting (PNS) attacks. Also, the imperfections in the measuring devices open a loophole for side channels that an eavesdropper may exploit to launch attacks such as large‐pulse attacks. As a result, this compromises the security of transmitted information. To address these challenges, the authors present a QKD protocol that is secure against both large‐pulse attacks and PNS attacks at zero‐error, in which the eavesdropper does not introduce any error, but still, the legitimate users of the system cannot distil a secure key. A notable feature of the proposed protocol is that it promotes greater robustness against both attacks than the Bennett‐Brassard 1984 (BB84) protocol or the Scarani‐Acin‐Ribordy‐Gisin 2004 (SARG04) protocol.
Integrated Photonics for Quantum Communications and Metrology

Over the last two decades, integrated photonics has profoundly revolutionized the domain of quantum technologies. The ongoing second quantum revolution stands as a timely opportunity for a state-of-the-art review and, most important, an exploration of the directions undertaken by integrated quantum photonics. Within this perspective, based on the recent advances, we discuss the current challenges and future trends related to different technological platforms. Key examples will be considered across various subfields, including quantum communication, quantum metrology, and quantum memories. Our discussion encompasses disruptive concepts, progress, and potential limitations. The main objective of this Perspective is to provide the reader with a forward-looking discussion ranging from state-of-the-art developments to open challenges of the field.Received 28 October 2022Revised 24 October 2023DOI:https://doi.org/10.1103/PRXQuantum.5.010101Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum computationQuantum correlations in quantum informationQuantum engineeringQuantum entanglementQuantum memoriesQuantum metrologyQuantum Information, Science & Technology
Education reform and change driven by digital technology: a bibliometric study from a global perspective

Abstract Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers &amp; Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.
The metaverse digital environments: A scoping review of the techniques, technologies, and applications

The “metaverse” has gained immense attention as the next internet frontier, holding significant economic implications, especially for the IT sector. To establish the groundwork for immersive metaverse spaces, our comprehensive scoping review uncovers the current techniques and technologies in metaverse development. This study investigates post-COVID-19 metaverse techniques, technologies, and applications, spanning from January 2020 to December 2022. Findings reveal potential metaverse applications across 11 industries, with education, manufacturing, healthcare, and real estate being notable. We identified 12 major technologies and four key development techniques that have gained prominence in the metaverse landscape. Notably, “Extended Reality (XR),” “Artificial Intelligence (AI),” and “Decentralized Technologies” were the three most frequently mentioned technologies, appearing in 73%, 40%, and 30% of the reviewed articles, respectively. Furthermore, “VR space convergence” and “fundamental technology” emerged as primary enablers in 73% and 43% of the selected articles, with “object connection” and “communication computing infrastructure” playing supportive roles. Integrating these technologies is vital for holistic metaverse development. Our report serves as a catalyst for discussions among stakeholders, promoting further research and the transformative potential of the metaverse. It highlights the need for advancing understanding and technological evolution in this emerging digital realm.