Reviewerly/exHarmony huggingface dataset:
On Sustainable Ring-Based Anonymous Systems

Anonymous systems (e.g. anonymous cryptocurrencies and updatable anonymous credentials) often follow a construction template where an account can only perform a single anonymous action, which in turn potentially spawns new (and still single-use) accounts (e.g. UTXO with a balance to spend or session with a score to claim). Due to the anonymous nature of the action, no party can be sure which account has taken part in an action and, therefore, must maintain an ever-growing list of potentially unused accounts to ensure that the system keeps running correctly. Consequently, anonymous systems constructed based on this common template are seemingly not sustainable. In this work, we study the sustainability of ring-based anonymous systems, where a user performing an anonymous action is hidden within a set of decoy users, traditionally called a “ring”. On the positive side, we propose a general technique for ring-based anonymous systems to achieve sustainability. Along the way, we define a general model of decentralised anonymous systems (DAS) for arbitrary anonymous actions, and provide a generic construction which provably achieves sustainability. As a special case, we obtain the first construction of anonymous cryptocurrencies achieving sustainability without compromising availability. We also demonstrate the generality of our model by constructing sustainable decentralised anonymous social networks. On the negative side, we show empirically that Monero, one of the most popular anonymous cryptocurrencies, is unlikely to be sustainable without altering its current ring sampling strategy. The main subroutine is a sub-quadratic-time algorithm for detecting used accounts in a ring-based anonymous system.
Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain's immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.
Knowledge, perceptions, and behavioral responses to earthquake early warning in Aotearoa New Zealand

Introduction Aotearoa New Zealand (NZ) experiences frequent earthquakes, with a history of damaging and fatal events, but currently does not have a national, official earthquake early warning (EEW) system. Since April of 2021, Google's Android Earthquake Alert System has operated independently in NZ. While recent work has identified general public support for such a system, it is important to assess public knowledge of EEW as well as typical responses to receiving an alert. The protective actions “Drop, cover, and hold” are recommended and taught in NZ and previous research found strong intentions to undertake these and other protective actions in response to an alert. Method However, it is important to explore a range of responses to these novel EEWs, including how much people know about them, what actions they took in response to the warning, and their overall judgment of the system including its usefulness. We undertook surveys following two widely received alerts from the Android Earthquake Alert System to assess public knowledge, perceptions, and responses to these alerts with a total sample size of 3,150. Results While most participants who received the alert found it useful, knowledge of both EEW generally and the Android System specifically was low and few participants used the time to protect themselves from shaking. Discussion These findings reiterate the importance of education and communication around a warning system, so that the public know how to act when they receive an alert.
REA, Triple-Entry Accounting and Blockchain: Converging Paths to Shared Ledger Systems

During the last half century, the concept of shared ledger systems that offer a single source of truth has challenged traditional bookkeeping, leading to innovations such as the resource-event-agent (REA) accounting framework, triple-entry accounting (TEA), and blockchain. Despite these advancements, the historical development of shared ledger systems remains under-researched and unclear, with the influence of REA on TEA particularly overlooked. This study aims to fill this gap by conducting a genealogical analysis of shared ledger systems, with a focus on tracing the development of TEA and its historical byproduct of the REA framework designed by McCarthy. Through a comprehensive literature review and interviews with pioneers in REA, TEA, and blockchain, we uncover the missing link between REA and TEA. Our findings suggest that the current explosion of shared ledger systems results from the convergence of three parallel research streams, occasionally interacting with each other. We correct common misconceptions, acknowledge the influence of key individuals, and map out the overlapping paths of REA, TEA, and blockchain. By elucidating the historical evolution of shared ledger systems, this study contributes to the academic debate and fosters further discourse among researchers in REA, TEA, and blockchain, thereby enhancing the potential applications within these fields.
Enhancing E-Learning with Blockchain: Characteristics, Projects, and Emerging Trends

Blockchain represents a decentralized and distributed ledger technology, ensuring transparent and secure transaction recording across networks. This innovative technology offers several benefits, including increased security, trust, and transparency, making it suitable for a wide range of applications. In the last few years, there has been a growing interest in investigating the potential of Blockchain technology to enhance diverse fields, such as e-learning. In this research, we undertook a systematic literature review to explore the potential of Blockchain technology in enhancing the e-learning domain. Our research focused on four main questions: (1) What potential characteristics of Blockchain can contribute to enhancing e-learning? (2) What are the existing Blockchain projects dedicated to e-learning? (3) What are the limitations of existing projects? (4) What are the future trends in Blockchain-related research that will impact e-learning? The results showed that Blockchain technology has several characteristics that could benefit e-learning. We also discussed immutability, transparency, decentralization, security, and traceability. We also identified several existing Blockchain projects dedicated to e-learning and discussed their potential to revolutionize learning by providing more transparency, security, and effectiveness. However, our research also revealed many limitations and challenges that could be addressed to achieve Blockchain technology’s potential in e-learning.
Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring

Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals' smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.
A stacked ensemble forecast for photovoltaic power plants combining deterministic and stochastic methods

Feature engineering (or creation of new features) that strengthens the importance of fundamental variables (e.g., cloud cover, partial shading, solar irradiance) is essential in gaining an accurate PV forecast for different use cases. On-grid and off-grid PV systems for residential prosumers, and larger scale PV systems are investigated from the PV forecast point of view. Therefore, in this paper, we propose a robust and scalable method designed for PV systems with various sizes and connectivity that provides accurate predictions. The originality of our research consists in feature engineering and combining the deterministic and stochastic models into a meta-learning Stacked Ensemble Forecast (SEF) method that is properly adjusted considering the connectivity of the PV system. The predictions of several standout Machine Learning (ML) algorithms are stacked and scaled to create an accurate forecast model. The proposed methodology is tested on three case studies that cover different types of PV systems: small (residential) on-grid, off-grid, and industrial PV power plants. In all cases, the SEF model provides accurate predictions with an R2 between 0.91 and 0.99 and MAPE between 0.1 and 0.29, followed closely by the stochastic models that have an average R2 of 0.85 and MAPE of 0.3. The deterministic model provides less accurate predictions with an average R2 of 0.8 and MAPE of 0.37. Furthermore, RMSE and MAE improved with more than 16% using the SEF model compared to the stochastic models and with more than 45% compared to the deterministic model.
HPDE: A dynamic Hierarchical Population based Differential Evolution with novel diversity metric

Differential Evolution (DE) is one of the most effective methods for single-objective numerical optimization, however, the former state-of-the-art DE variants ignore the huge differences among the individuals in the population. To take full advantage of different individuals in the population, in this paper, a dynamic Hierarchical Population based Differential Evolution (HPDE) with novel diversity metric is proposed, and the HPDE algorithm has the following innovations: Firstly, the individuals in the hierarchical population are dynamically divided into two layers, the layer of elites, and the layer of ordinary individuals. Moreover, the ordinary layer individuals are further divided into secondary elites and inferior individuals. The individuals in the two layers employed different mutation strategies and the corresponding parameter control schemes. Secondly, a novel hierarchical population based mutation strategy is firstly proposed in our HPDE algorithm and the individuals in the elite layer employ the novel mutation strategy aiming at scouting the landscape of the objective while the ordinary individuals employ an improved "DE/target-pbest/1/bin" strategy aiming at getting a better balance between exploration and exploitation of the solution space. Thirdly, a Novel Parameter Control technique, namely NPC technique, is employed in the adaptation of control parameters during the evolution. Fourthly, a new metric of population diversity is proposed and premature convergence can be avoided by maintaining better population diversity. To evaluate the performance of our HPDE algorithm, intensive experiments are conducted on 88 benchmark functions from the CEC2013, CEC2014 and CEC2017 test suites, and the results show the competitiveness of our HPDE with six recent state-of-the-art DE variants, e.g. it obtained 28 similar or better performance improvements out of the total 30 benchmarks on 30D optimization in comparison with the winner algorithm, the LSHADE algorithm, of the CEC2014 competition and it also obtained 19 similar or better performance improvements out of the total 30 benchmarks on 30D optimization in comparison with the winner DE variant, the jSO algorithm, of the CEC2017 competition. Furthermore, the novel HPDE algorithm is utilized to estimate the parameters of the photovoltaic model, and the experiment results support its superiority as well.
Security of quantum key distribution from generalised entropy accumulation

Abstract The goal of quantum key distribution (QKD) is to establish a secure key between two parties connected by an insecure quantum channel. To use a QKD protocol in practice, one has to prove that a finite size key is secure against general attacks: no matter the adversary’s attack, they cannot gain useful information about the key. A much simpler task is to prove security against collective attacks, where the adversary is assumed to behave identically and independently in each round. In this work, we provide a formal framework for general QKD protocols and show that for any protocol that can be expressed in this framework, security against general attacks reduces to security against collective attacks, which in turn reduces to a numerical computation. Our proof relies on a recently developed information-theoretic tool called generalised entropy accumulation and can handle generic prepare-and-measure protocols directly without switching to an entanglement-based version.
A Model-Free Feature Selection Technique of Feature Screening and Random Forest-Based Recursive Feature Elimination

This paper studies data with mass features, commonly observed in applications such as text classification and medical diagnosis. We allow data to have several structures without requiring a specific model and propose an efficient model-free feature selection procedure. The proposed method can work with various types of datasets. We demonstrate that this method has several desirable properties, including high accuracy, model-free, and computational efficiency and can be applied to practical problems with different modelings. We prove that the proposed method achieves selection consistency and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </math> consistency under mild regularity conditions. We conduct simulations on various datasets, including data generated from the generalized linear model, additive model, Poisson regression, and binary classification model. These simulations illustrate the superior performance of the proposed method compared to other existing methods across different model settings. In addition, we apply our method to two real examples, the Tecator dataset and the Daily Demand Orders dataset, both of which are continuous and high dimensional. In both cases, our method consistently achieves high accuracy in prediction and model selection.
SplitGP: Achieving Both Generalization and Personalization in Federated Learning

A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i.e., to individual clients) and generalization (i.e., to unseen data) properties concurrently. Existing techniques in federated learning (FL) have encountered a steep tradeoff between these objectives and impose large computational requirements on edge devices during training and inference. In this paper, we propose SplitGP, a new split learning solution that can simultaneously capture generalization and personalization capabilities for efficient inference across resource-constrained clients (e.g., mobile/IoT devices). Our key idea is to split the full ML model into client-side and server-side components, and impose different roles to them: the client-side model is trained to have strong personalization capability optimized to each client’s main task, while the server-side model is trained to have strong generalization capability for handling all clients’ out-of-distribution tasks. We analytically characterize the convergence behavior of SplitGP, revealing that all client models approach stationary points asymptotically. Further, we analyze the inference time in SplitGP and provide bounds for determining model split ratios. Experimental results show that SplitGP outperforms existing baselines by wide margins in inference time and test accuracy for varying amounts of out-of-distribution samples.
Adoption of blockchain technology in hospitality and tourism industry and sustainability performance: impact of technological turbulence and senior leadership support

Purpose The rapid increase of use of online platforms by the customers in the hospitality and tourism industry has invited the needs for using digital platforms by the concerned industry. In such a scenario, the purpose of this study is to examine how adoption of blockchain technology in hospitality and tourism industry could impact the sustainability performance of the organizations under the moderating influence of technological turbulence and senior leadership support. Design/methodology/approach With the help of existing literature, stakeholder theory and dynamic capability view (DCV), a theoretical model is proposed. It was validated using the PLS-SEM technique with 311 respondents who have different managerial positions in the hospitality and tourism industry. The proposed theoretical model is unique and effective as it has high explanatory power. Findings The study demonstrates the importance of adopting BCT in the hospitality and tourism sector and how it could improve the sustainability performance of organizations in that sector. This study also finds that there is a significant moderating impact of technological turbulence and senior leadership support on such organizations that adopt BCT. Research limitations/implications This study provides valuable inputs to practitioners in the industry by showing how adopting BCT can improve their sustainability performance. The study also demonstrates that leaders and the managers should support adopting BCT in their organizations and they can help to overcome any technological challenges that might come up while adopting it. Originality/value The present study proposes a unique theoretical model which was also validated using a statistical approach. Moreover, both stakeholder theory and dynamic capability view were integrated to propose the theoretical model, which is a novel attempt so far as adoption of BCT in hospitality and tourism industry is concerned.
Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network

With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the Vision Transformer (ViT), i.e., extracting global features to detect deepfake images effectively. After conducting comprehensive experiments, our method demonstrates a high level of effectiveness, achieving a detection accuracy, precision, recall, and F1 rate of 99.5 to 100% for both the original and mixture data set. According to our existing understanding, this study is a research endeavor incorporating real-world applications, specifically examining Snapchat-filtered images.
Revolutionizing Cyberbullying Prevention: A Cutting-Edge Natural Language Processing-Based Approach

The rise of social media has brought about new cybersecurity threats that can affect individuals and organizations. To ensure safe social media usage, there is a need for effective cybersecurity applications that can mitigate these threats. Cyberbullying is a serious issue that affects many individuals, particularly young people, and can have long-lasting effects on mental health and well-being. Natural language processing (NLP) techniques can be used to detect cyberbullying in online content. This study utilized the cyberbullying_tweets.csv dataset from Kaggle and implemented two classifiers, Logistic Regression and Decision Tree, to detect cyberbullying in tweets. The results showed that Logistic Regression achieved an accuracy of 91%, while Decision Tree achieved an accuracy of 89%. These findings highlight the potential of NLP techniques to accurately detect cyberbullying and help prevent this harmful behavior on social media platforms.
Mitigating Class Imbalance in Sentiment Analysis through GPT-3-Generated Synthetic Sentences

In this paper, we explore the effectiveness of the GPT-3 model in tackling imbalanced sentiment analysis, focusing on the Coursera online course review dataset that exhibits high imbalance. Training on such skewed datasets often results in a bias towards the majority class, undermining the classification performance for minority sentiments, thereby accentuating the necessity for a balanced dataset. Two primary initiatives were undertaken: (1) synthetic review generation via fine-tuning of the Davinci base model from GPT-3 and (2) sentiment classification utilizing nine models on both imbalanced and balanced datasets. The results indicate that good-quality synthetic reviews substantially enhance sentiment classification performance. Every model demonstrated an improvement in accuracy, with an average increase of approximately 12.76% on the balanced dataset. Among all the models, the Multinomial Naïve Bayes achieved the highest accuracy, registering 75.12% on the balanced dataset. This study underscores the potential of the GPT-3 model as a feasible solution for addressing data imbalance in sentiment analysis and offers significant insights for future research.