Reviewerly/exHarmony huggingface dataset:
Subsidy Bridge: Rewarding Cross-Blockchain Relayers with Subsidy

Cross-chain technology aims to enable interoperability between isolated blockchains. However, existing cross-chain solutions cannot achieve both decentralization and incentive compatibility. In the paper, we introduce Subsidy Bridge, a general and decentralized relay scheme with special incentive design similar to Bitcoin mining. In Subsidy Bridge, target chain carries out cross-chain validation relying on relayers submitting new block headers of source chain, while honest relayers obtain basic subsidy from target chain and transaction fee from cross-chain users. The utility of honest relayers is always positive even when users are temporarily inactive. Analysis results demonstrate that our solution can provide decentralization, incentive compatibility, and strong security.
Towards Quantifying Cross-Domain Maximal Extractable Value for Blockchain Decentralisation

In the research society, many solutions to solving blockchain scaling have been tried historically, usually by compromising on decentralisation. Ethereum has chosen to scale by switching to Proof of Stake (PoS) consensus and adding data sharding to allow Layer 2 execution to be cheaper. However, in the light of cross-domain Maximal Extractable Value (MEV), even this strategy may have centralising forces built-in. In this work, we focus on cross domain MEV and try to identify cross domain arbitrage. We achieve this by extracting Uniswap data from four different domains and analysing the dataset with two different methods. Based on the analysis, we are successful in identifying one smart contract's address, which is deployed to multiple domains, engaging in cross domain arbitrage. We also illustrate the difficulties when quantifying cross-domain MEV in practice.
PiXi: Password Inspiration by Exploring Information

Passwords, a first line of defense against unauthorized access, must be secure and memorable. However, people often struggle to create secure passwords they can recall. To address this problem, we design Password inspiration by e Xploring information (PiXi), a novel approach to nudge users towards creating secure passwords. PiXi is the first of its kind that employs a password creation nudge to support users in the task of generating a unique secure password themselves. PiXi prompts users to explore unusual information right before creating a password, to shake them out of their typical habits and thought processes, and to inspire them to create unique (and therefore stronger) passwords. PiXi’s design aims to create an engaging, interactive, and effective nudge to improve secure password creation. We conducted a user study ( $$N=238$$ ) to compare the efficacy of PiXi to typical password creation. Our findings indicate that PiXi’s nudges do influence users’ password choices such that passwords are significantly longer and more secure (less predictable and guessable).
Reading in the city: mobile eye-tracking and evaluation of text in an everyday setting

Reading is often regarded as a mundane aspect of everyday life. However, little is known about the natural reading experiences in daily activities. To fill this gap, this study presents two field studies (N = 39 and 26, respectively), where we describe how people explore visual environments and divide their attention toward text elements in highly ecological settings, i.e., urban street environments, using mobile eye-tracking glasses. Further, the attention toward the text elements (i.e., shop signs) as well as their memorability, measured via follow-up recognition test, were analysed in relation to their aesthetic quality, which is assumed to be key for attracting visual attention and memorability. Our results revealed that, within these urban streets, text elements were looked at most, and looking behaviour was strongly directed, especially toward shop signs, across both street contexts; however, aesthetic values were not correlated either with the most looked at signs or the viewing time for the signs. Aesthetic ratings did however have an effect on memorability, with signs rated higher being better recognised. The results will be discussed in terms aesthetic reading experiences and implications for future field studies.
Semi-Honest 2-Party Faithful Truncation from Two-Bit Extraction

As a fundamental operation in fixed-point arithmetic, truncation can bring the product of two fixed-point integers back to the fixed-point representation. In large-scale applications like privacy-preserving machine learning, it is essential to have faithful truncation that accurately eliminates both big and small errors. In this work, we improve and extend the results of the oblivious transfer based faithful truncation protocols initialized by Cryptflow2 (Rathee et al., CCS 2020). Specifically, we propose a new notion of two-bit extraction that is tailored for faithful truncation and demonstrate how it can be used to construct an efficient faithful truncation protocol. Benefiting from our efficient construction for two-bit extraction, our faithful truncation protocol reduces the communication complexity of Cryptflow2 from growing linearly with the fixed-point precision to logarithmic complexity. This efficiency improvement is due to the fact that we reuse the intermediate results of eliminating the big error to further eliminate the small error. Our reuse strategy is effective, as it shows that while eliminating the big error, it is possible to further eliminate the small error at a minimal cost, e.g., as low as communicating only an additional 160 bits in one round.
Outsourcing Verifiable Distributed Oblivious Polynomial Evaluation from Threshold Cryptography

Distributed oblivious polynomial evaluation (DOPE) is a variant of two-party computation where a sender party $$P_1$$ has a polynomial f(x) of degree k and the receiver party $$P_2$$ holds an input $$\alpha $$ . They conduct a secure computation with a number of t distributed cloud servers such that $$P_2$$ obtains the correct output $$f(\alpha )$$ while the privacy of the inputs is preserved. This system is the building block of many cryptographic models and machine learning algorithms. We propose a lightweight DOPE scheme with two separate phases: setup and computation, which means that the setup phase can be executed at any time before the actual computation phase. The number of the servers (t) does not depend on the polynomial degree (k), and the main expensive computation is securely outsourced to the cloud servers using the idea of threshold cryptography. As a result, any normal user with low computational power devices (e.g., mobile, laptop, etc.) would be able to evaluate and verify the output over a large field while the security conditions are preserved. Our protocol maintains the security against a static active adversary corrupting a coalition of up to $$t-1$$ servers and the opposed party. The main two parties commit to their inputs using non-interactive zero-knowledge proof techniques. The communication complexity is linear and bounded to O(t) field elements which means that, unlike the previous studies in this field, it does not depend on the polynomial degree k.
Improved Related-Key Rectangle Attack Against the Full AES-192

AES is currently the most important block cipher. There are three variants, i.e., AES-k with $$k\in \{128, 192, 256\}$$ denoting the key size in bits. At ASIACRYPT 2009, Biryukov et al. carried out the rectangle attack against the full AES-192 and achieved the best results under the related-key setting so far. During our research, we found that the time complexity of each phase in the attack proposed by Biryukov et al. is unbalanced. More specifically, the time complexity of the quartet processing phase far exceeds that of the other phases. Therefore, the key of our work is to balance the time complexity of each phase so that the overall time complexity of the attack against the full AES-192 is reduced. In this paper, we adopt a strategy of pre-guessing some subkey bits. Indeed, pre-guessing subkeys increase the time complexity of some phases, but we can get more filter bits to reduce the time complexity of processing quartets. Using the above concepts, the time complexity of the rectangle key recovery attack on full AES-192 under the related-key setting can be reduced from $$2^{176}$$ to $$2^{158}$$ .
A Polynomial-Time Attack on G2SIDH

Supersingular isogeny Diffie-Hellman key exchange protocol (SIDH) is the most concerned isogeny-based protocol resisting quantum attacks, and in 2019 Flynn and Ti implemented a dimension two version (G2SIDH). However, at EUROCRYPT’23, Castryck and Decru, Maino et al., and Robert proposed efficient attacks against SIDH. Moreover, Robert extended his attacks to high-dimensional SIDH in theory. In this paper, we, for the first time, find that the uniqueness of isogeny decomposition and computing intermediate isogeny through kernel only hold for one class of high-dimensional isogenies. Besides, we prove a counting formula about isogenies between general abelian varieties. Based on these theoretic results, we present complete steps of parameter tweaks in attacks against high-dimensional SIDH, and analyze the efficiency of each tweak. In particular, for Flynn and Ti’s G2SIDH, we construct two attack algorithms that can recover the secret key in polynomial time. Our paper demonstrates the differences between isogenies in dimension one and higher dimensions, and illustrates that all high-dimensional SIDH protocols are insecure.
From data to theory

Abstract The semantic classification of over 2,200 constructions in the Russian Constructicon has emerged objectively from empirical analysis. The resulting semantic classification comports with and goes beyond frame semantics, revealing complex patterns of related constructions verified against corpus data and by a panel of native speakers. Our model of a constructicon can inform and complement existing approaches with additional findings. We detail our discovery procedure and the semantic relationships that link constructions. While our methods and findings are based on a single language, they can serve as a basis for cross-linguistic comparison in the field of Construction Grammar and constructicography research.
Establishment and Cluster Analysis of Performance Evaluation Index System for New Retail Enterprises Driven by Blockchain Technology

Over the last few years, the further transfomation of retailing derived from growing development and integration of Internet has led to the emergence of new retail mode as a tendency conforming to the demand of the consumer market. Blockchain technology as a major innovation in supply chain has a wide prospect and only little investigation about its application is available in the literature. Literature and expert opinion interpretation in index are further analyzed and modeled using fuzzy-interpretive structural modeling (FISM), Fuzzy Matriced Impacts Corises-Multiplication Appliance Classment (FMICMAC). The combined approach of both FISM and FMICMAC is applied to identify the common drivers to enhance the efficiency of new retailing in the light of efficient supply chain management. The findings in the article endorse that the traditional retailing can be made efficient by integrating the blockchain technology considering three most driving characteristics, namely, Effective Information, Interactive Reflection and Information Circulation. The study carried out in this article motivates industries to implement blockchain technology in their new retailing model. It will reduce the transaction cost, documentation work, save time and eliminate human error. The common characteristics identified in this proposed work would help in managerial decisions for the adoption of blockchain technology to ensure that the system becomes more transparent, easily traceable and finally improves the performance.
An assessment of cybersecurity performance in the Saudi universities: A Total Quality Management approach

Cybersecurity systems are crucial for safeguarding information assets across various sectors, including government, military, and commercial domains. In Saudi Arabia, cybersecurity has gained significant importance within the national security strategy, resulting in substantial investments in technologies to protect information assets, combat cyber threats, and preserve privacy. In light of Protection Motivation Theory, it is assumed that evaluating the performance of cybersecurity policies and measures (threat appraisal) is vital for their effective implementation (coping appraisal). This study focuses on evaluating the cybersecurity performance of Saudi universities. Employing a mixed-methods design, the study utilizes questionnaires and interviews to collect data. The participants include representatives from 10 Saudi universities, with 107 respondents for the questionnaire phase and 20 participants for the interviews. Diverse job categories and levels within the universities are represented to gather valuable insights from individuals with expertise in cybersecurity and Total Quality Management (TQM) processes. Findings showed that there is room for improvement in the cybersecurity practices of Saudi universities. Only a minority of participants reported regular risk assessments and timely addressing of identified risks. Additionally, participants expressed concerns about the lack of well-defined policies and procedures, insufficient training and awareness programs, and non-compliance with cybersecurity regulations and standards. A significant percentage of participants rated their organization’s cybersecurity performance as average or poor. However, the majority of participants affirmed the importance of cybersecurity in relation to strategic objectives and Total Quality Management. The study stressed the need for comprehensive approaches to cybersecurity, including risk assessment, policy development, training, compliance, and continuous monitoring.
Piecewise Linear Approximation-Driven Primal SVM Approach for Improved Iris Classification Efficiency

Classification, a crucial aspect of machine learning, revolves around the meticulous analysis of data. However, the complexity of diverse life forms on Earth poses a challenge in distinguishing species that share similar attributes. The iris flower, with its subspecies exemplifies this challenge. The aim of the paper is to develop a methodology that not only enhances classification accuracy but also effectively addresses computational efficiency, facilitating faster and more practical categorization of iris patterns. This novel approach named Piecewise Linear Approximation based SVM (PLA-SVM) is applied to flower species classification and is benchmarked against alternative machine learning techniques. Implementation is carried out utilizing MATLAB – GUROBI interface of and GUROBI Solver. The performance metrics such as accuracy, precision, F1 score and ROC – AUC Curve are used to compare proposed algorithm performance. This comprehensive analysis enables a comparative study of diverse algorithms, ultimately validating the proposed PLA-SVM technique using the Iris dataset. The numerical implementation results shows that the PLASVM outperforms the existing standard classifiers in terms of different performance matrices.
Collaborative Augmented Reality in Higher Education Settings – Strategies, Learning Outcomes and Challenges

Augmented reality (AR) has the potential to enhance the learning experience of students by providing collaborative, interactive, and immersive environments. This paper reports a systematic literature review focused on examining the research studies on the use of AR in higher education from January 2018 to October 2022, specifically in the context of collaborative learning. The initial search resulted in a total of 2537 studies, of which 20 were analyzed for final review. The main findings suggest that learning using AR-enabled collaborative learning benefits students’ overall knowledge gain, improves task performance, reduces task errors, and provides a positive collaboration experience in higher education settings. This article concludes by discussing the implications of these findings and their use as guidelines by educators, designers, and researchers for developing effective collaborative AR learning content.
An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout

Given the shortage of intensive care units (ICUs) due to the coronavirus disease (COVID-19), a prediction model is essential in ensuring the availability of ICU beds. However, several challenges, such as the importance of distinguishing indicators, efficiency of ICU admission records, and the explainability and effectiveness of the prediction model, hinder the effective prediction of ICU admissions. To mitigate these challenges, an explainable decision model that uses the extended belief rule-based system is introduced to predict ICU admission. First, an indicator extraction model is proposed to measure the importance of the various indicators and obtain representative indicators. Second, a Charnes, Cooper, and Rhodes (CCR) model is constructed to measure the efficiencies of the belief rules to achieve the compact structure of an extended belief rule base. Third, a new extended belief rule-based model, optimized by parameter optimization and domain division-based rule reduction, is developed to predict ICU admission. These procedures enable the explainable decision model to adapt to big data situation, offer explanations and realize high efficiency. Finally, a case study of ICU admission during a COVID-19 outbreak is conducted to demonstrate the implementation and effectiveness of the proposed model in a comparative analysis.
Quantum image denoising: a framework via Boltzmann machines, QUBO, and quantum annealing

We investigate a framework for binary image denoising via restricted Boltzmann machines (RBMs) that introduces a denoising objective in quadratic unconstrained binary optimization (QUBO) form well-suited for quantum annealing. The denoising objective is attained by balancing the distribution learned by a trained RBM with a penalty term for derivations from the noisy image. We derive the statistically optimal choice of the penalty parameter assuming the target distribution has been well-approximated, and further suggest an empirically supported modification to make the method robust to that idealistic assumption. We also show under additional assumptions that the denoised images attained by our method are, in expectation, strictly closer to the noise-free images than the noisy images are. While we frame the model as an image denoising model, it can be applied to any binary data. As the QUBO formulation is well-suited for implementation on quantum annealers, we test the model on a D-Wave Advantage machine, and also test on data too large for current quantum annealers by approximating QUBO solutions through classical heuristics.
Enhanced intra-inter view correlation learning for multi-view subspace clustering

Multi-view subspace clustering has attracted widespread attention for its superior clustering effectiveness. However, most of the current methods do not fully exploit the intra-view (sample-to-sample) and inter-view (view-to-view) information among multi-view data. Focusing on this problem, we propose a method called enhanced intra-inter view correlation learning for multi-view subspace clustering (EIVCL). EIVCL simultaneously considers the intra-view and inter-view correlations and introduces two constraint terms for each aspect to capture the data structure information. Specifically, in the intra-view space, we apply the tensor-singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on the tensor formed by stacking self-representative coefficient matrices for obtaining the high-order correlation information between samples in the specific view. Moreover, we introduce a hypergraph-induced Laplace regularization term to preserve the local geometric structure within views. In the inter-view space, we impose the t-SVD-based TNN on the rotated tensor to obtain the multiple views correlation. Furthermore, we utilize the kernel dependence metric, namely Hilbert–Schmidt independence criterion, to capture the high-order non-linear relationships between views. In addition, all above strategies are integrated into a unified clustering framework, which is solved by our proposed optimization algorithm based on the alternating direction method of multipliers. Extensive experiments on six benchmark datasets demonstrate that EIVCL outperforms several state-of-the-art multi-view algorithms.
Weathering and weaponizing the #TwitterPurge: digital content moderation and the dimensions of deplatforming

Since 2016, news outlets across the political spectrum have increased coverage of social media content moderation and given the term "deplatforming" unprecedented mainstream prominence. This discourse has emphasized individual deplatforming; the process by which an individual's account on a platform is suspended, disallowing their access to the platform's user base and services. However, this individual emphasis draws attention away from community deplatforming, which is of primary significance. Deplatforming functions as active moderation. Beyond individual deplatforming, we must recognize incremental deplatforming – cumulatively affecting entire communities over a span of time – and implosive deplatforming – instantaneously erasing entire platforms, websites, and services.
Metaphysical and dialectical aspects in the reality of myth

The aim of the research is to identify the relationship between the metaphysical and dialectical aspects in the reality of myth by determining the characteristics of its development and establishing connections within it. The paper reveals the dynamic aspect of mythological events, examines the specificity of the interaction between individual myths, the relationship between mythological and historical time. The scientific novelty of the research lies in overcoming the dichotomy of the metaphysical/dialectical in relation to the reality of myth and proposing a metaphysical model for the development of constructed realities. As a result, it has been proved that myth possesses a unique dynamics that combines metaphysical and dialectical aspects. A model for the development of mythological reality based on connections mediated by mythemes and archetypes, which acquires temporal characteristics through its inclusion in the structure of reality, has been identified.