Reviewerly/exHarmony huggingface dataset:
Towards Efficient and Privacy-Preserving Anomaly Detection of Blockchain-Based Cryptocurrency Transactions

In recent years, a growing number of breaches targeting cryptocurrency exchanges have damaged the credibility of the entire cryptocurrency ecosystem. To prevent further harm, it’s crucial to detect the anomalous behaviors hidden within cryptocurrency transactions and offer predictive suggestions. However, details of transaction records must be carefully analyzed for effective detection, and this information could be exploited by adversaries to launch attacks such as de-anonymization and model interference. As a result, it is essential to prioritize privacy preservation when designing an anomaly detection system for cryptocurrency transactions. In this paper, we propose a privacy-preserving anomaly detection (PPad) scheme for cryptocurrency transactions based on a decision tree model, which achieves privacy preservation by using additively homomorphic encryption and matrix perturbation techniques. We also design and implement PPad’s underlying protocol in a cloud outsourcing environment. The correctness and privacy properties of PPad have been proven through detailed analysis. Experimental results show that our scheme can offer privacy assurance with desirable detection effectiveness and efficiency, making it suitable for real-world applications.
Graph Unlearning Using Knowledge Distillation

With the popularity of graph-structured data and the promulgation of various data privacy protection laws, machine unlearning in Graph Convolutional Network (GCN) has attracted more and more attention. However, machine unlearning in GCN scenarios faces multiple challenges. For example, many unlearning algorithms require large computational resources and storage space or cannot be applied to graph-structured data, and so on. In this paper, we design a novel, lightweight unlearning method using knowledge distillation to solve the class unlearning problem in GCN scenarios. Unlike other methods using knowledge distillation to unlearn Euclidean spatial data, we use a single retrained deep Graph Convolutional Network via Initial residual and Identity mapping (GCNII) model as the teacher network and the shallow GCN model as a student network. During the training stage, the teacher’s network transfers the knowledge of the retained set to the student network, enabling the student network to forget some or more categories of information. Compared with the baseline methods, Graph Unlearning using Knowledge Distillation (GUKD) shows state-of-the-art model performance and unlearning quality on five real datasets. Specifically, our method outperforms all baseline methods by 33.77% on average in the multi-class experiments on the Citeseer dataset.
Zoomer: A Website Fingerprinting Attack Against Tor Hidden Services

The deanonymization of Tor hidden services (HS) is the top priority for dark web governance. Thanks to the leap of artificial intelligence technology, it is a promising and feasible direction to launch a website fingerprint attack (WFA) by deep learning to identify the access traffic of HS. However, unlike public services (PS) on the surface network, the web pages of HS have simple structures, limited content, and similar development templates. Thus, it is different to extract effective features from the access traffic for HS identification. In addition, many WFA methods cannot capture global features from access traffic because their convolutional neural networks (CNN) lack the ability of long-distance modeling. Aiming at the shortcomings, we propose Zoomer, a novel WFA method with a scalable perspective when extracting features. The contribution of our work lies in three points. Firstly, a burst-based HS fingerprint generation method is proposed to describe the sequence of resource access. Secondly, a new WFA model is designed by introducing global burst attention (GBA) into the classic structure of CNN for global feature extraction. Finally, comparison experiments are conducted in both closed-world and open-world scenarios. The results show that our Zoomer outperforms three state-of-the-art WFA methods.
Can Trustless Protocols Guarantee Price Stability?

Decentralized, trustless protocols cannot provably maintain stable value with respect to real-world assets. The best that can be achieved with Bitcoin-like assets is some combination of stable equilibrium, probabilistic guarantees and minimized-but-non-zero trust.
Levenberg-Marquardt Algorithm Applied for Foggy Image Enhancement

In this paper, we introduce a novel Model Based Foggy Image Enhancement using Levenberg-Marquardt non-linear estimation (MBFIELM). It presents a solution for enhancing image quality that has been compromised by homogeneous fog. Given an observation set represented by a foggy image, it is desired to estimate an analytical function dependent on adjustable variables that best cross the data in order to approximate them. A cost function is used to measure how the estimated function fits the observation set. Here, we use the Levenberg-Marquardt algorithm, a combination of the Gradient descent and the Gauss-Newton method, to optimize the non-linear cost function. An inverse transformation will result in an enhanced image. Both visual assessments and quantitative assessments, the latter utilizing a quality defogged image measure introduced by Liu et al. (2020), are highlighted in the experimental results section. The efficacy of MBFIELM is substantiated by metrics comparable to those of recognized algorithms like Artificial Multiple Exposure Fusion (AMEF), DehazeNet (a trainable end-to-end system), and Dark Channel Prior (DCP). There exist instances where the performance indices of AMEF exceed those of our model, yet there are situations where MBFIELM asserts superiority, outperforming these standard-bearers in algorithmic efficacy.
Ontology of the Universal Subspace of Common Digital Space of Scientific Knowledge

The work is a development of research conducted by the authors in the field of creating a Common Digital Space of Scientific Knowledge (CDSSK). In the framework of previous studies, a unified structure for representing the ontology of the elements of the CDSSK (subspaces, classes and attributes of objects, relationships between objects or attributes) was proposed. This paper presents a variant of the ontology of the universal subspace (AXES) of the CDSSK, proposed by the authors, built in accordance with the developed structure. There are defined 10 subject and 10 auxiliary classes of objects In the described model of USS. Among the subject classes are “persons”, “publications”, “museum objects”, “events”, etc. Among the auxiliary ones are “formats”, “units of measurement”, “languages”, etc. The paper contains reference book of each class, is built in accordance with the ontology structure model, a list of object attributes, their directories and examples of static dictionaries.
Investigating the Use of Online Bilingual Resources by Translation Students: The Vertical/Sequential Model Revisited

Four experiments were carried out in a university translation course after noticing the recurrence of specific errors identical in most target texts produced by students. The purpose was, to identify the errors and verify whether they could be attributed to using online bilingual resources before comprehension of the source text, and, second, to implement a teaching strategy for addressing this problem and later assess its effectiveness. Participants in the study were twenty-seven female students in two groups registered for the course in two semesters: a group in one semester and the other in the following term. They translated two experimental texts from English L2 into Arabic L1. Each text was translated twice: the first time by one group at the beginning of their course, where students were allowed to access all types of resources, and the second by the other group towards the end of their course, after implementation of the teaching strategy. Comparison between the target texts produced before the implementation of the strategy and those made following the strategy reveals that the latter category contained f fewer translation errors -regrouped in the study as inappropriate renderings, with almost no occurrence of the type termed excessive literality. The findings suggest that the use of bilingual resources by translation students before full-text comprehension is the main cause of most otherwise avoidable errors. Consequently, it should be banned at the first phase of the translation process. The teaching strategy, based on the vertical/sequential translating model, is recommended in translation teaching.
Revisiting the Deep Learning-Based Eavesdropping Attacks via Facial Dynamics from VR Motion Sensors

Virtual Reality (VR) Head Mounted Display’s (HMD) are equipped with a range of sensors, which have been recently exploited to infer users’ sensitive and private information through a deep learning-based eavesdropping attack that leverage facial dynamics. Mindful that the eavesdropping attack employs facial dynamics, which vary across race and gender, we evaluate the robustness of such attack under various users characteristics. We base our evaluation on the existing anthropological research that shows statistically significant differences for face width, length, and lip length among ethnic/racial groups, suggesting that a “challenger” with similar features (ethnicity/race and gender) to a victim might be able to more easily deceive the eavesdropper than when they have different features. By replicating the classification model in [17] and examining its accuracy with six different scenarios that vary the victim and attacker based on their ethnicity/race and gender, we show that our adversary is able to impersonate a user with the same ethnicity/race and gender more accurately, with an average accuracy difference between the original and adversarial setting being the lowest among all scenarios. Similarly, an adversary with different ethnicity/race and gender than the victim had the highest average accuracy difference, emphasizing an inherent bias in the fundamentals of the approach through impersonation.
Seasonal Adjustment of Weekly Data by Discounted Least Squares in R

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Cryptanalysis of Two White-Box Implementations of the CLEFIA Block Cipher

The CLEFIA block cipher has a generalised Feistel structure, which has been an ISO international standard since 2012. In 2014 Su et al. proposed a white-box CLEFIA implementation with a white-box table for an S-box, and in 2020 Yao et al. presented an algebraic attack on Su et al.’s implementation with a time complexity of $$2^{30}$$ and proposed another white-box CLEFIA implementation with a basic white-box table for two S-boxes. In this paper, we apply Lepoint et al.’s collision-based attack method to Su et al.’s implementation and recover all the white-box operations and the round and whitening keys with a time complexity of about $$2^{22}$$ S-box computations, and analyse the security of Yao et al.’s implementation against Lepoint et al.’s collision-based attack method. For Yao et al.’s implementation, on one hand, our experiment under a small fraction of (affine encodings, round key) combinations suggests that it can resist Lepoint et al.’s collision-based attack method, for the rank of the concerned linear system is much less than the number of the involved unknowns, but on the other hand, it is not clear whether there exist affine encodings such that the rank of the corresponding linear system is slightly less than the number of the involved unknowns, for which case Lepoint et al.’s method can be applied to remove most white-box operations until mainly some Boolean masks remain. We also experimentally test that the rank of the concerned linear system is invariant when the Boolean encodings are changed to affine encodings in our attack on Su et al.’s implementation. Our cryptanalysis suggests to some extent that for white-box CLEFIA implementation, building a white-box table with two S-boxes is preferable to building a white-box table with a single S-box in the sense of their security against Lepoint et al.’s collision-based attack method, but nevertheless we leave it as an open problem to investigate the distribution of the ranks under all encodings.
Virtual exhibition as a model of an element of the Common Digital Space of Scientific Knowledge

The article is devoted to the virtual exhibitions formation in order to popularize science, provide access to a variety of information in various fields of science, as well as to attract attention to current problems and achievements in the scientific field, as well as the integration of such exhibitions into a Common Digital Space of Scientific Knowledge. The article formulates the basic principles of the formation of virtual exhibitions, namely the approaches to the content selection, the allocation of the main sections, the typification of virtual exhibitions (autonomous, remote combined) is proposed. The methodology of the formation of virtual exhibitions in the MSC RAS is considered. As an example of an interdepartmental combined virtual exhibition containing heterogeneous content, the virtual exhibition "Madame Penicillin" dedicated to the penicillin creator Z.V. Yermolyeva is described in detail.
Integrating a Blockchain-based Dark Web in the Metaverse

This document gives a scenario and ways to implement that scenario. And also, the implication if applied to scenarios in real life. Dark web and metaverse together? Their combination could just restructure the whole world. Users will find Metaverse safer as their identity and IP address will be safe all the time. The Dark web has the potential to add a layer of shield to protect end users from cyber-attacks. But this fusion will also encourage the threat of illegal activities.
TimeClave: Oblivious In-Enclave Time Series Processing System

Cloud platforms are widely adopted by many systems, such as time series processing systems, to store and process massive amounts of sensitive time series data. Unfortunately, several incidents have shown that cloud platforms are vulnerable to internal and external attacks that lead to critical data breaches. Adopting cryptographic protocols such as homomorphic encryption and secure multi-party computation adds high computational and network overhead to query operations. We present TimeClave, a fully oblivious in-enclave time series processing system: TimeClave leverages Intel SGX to support aggregate statistics on time series with minimal memory consumption inside the enclave. To hide the access pattern inside the enclave, we introduce a non-blocking read-optimised ORAM named RoORAM. TimeClave integrates RoORAM to obliviously and securely handle client queries with high performance. With an aggregation time interval of 10 s, $$2^{14}$$ summarised data blocks and 8 aggregate functions, TimeClave run point query in 0.03 ms and a range query of 50 intervals in 0.46 ms. Compared to the ORAM baseline, TimeClave achieves lower query latency by up to $$2.5\times $$ and up to $$2\times $$ throughput, with up to 22K queries per second.
The Fractal Quantum Gravity

Based on the smallest physical constant of the product of space interval, time interval, and energy, the fractal quantum gravity (FQG) theory has demonstrated that every particle or physical system consists of these smallest units in fractal structures. The general relativity is an approximation of the FQG equation when the quantum effect is negligible, while the quantum theory is an approximation of the FQG equation when the interaction between space, time, and energy is very weak or negligible. The stationary-action principle can be derived from the FQG equation. The mass range of possibly existing elementary particles and an accelerating expansion evolution model of the universe can be obtained through the FQG equation. This FQG equation satisfied almost all the requirements of a quantum gravity theory and there is no free constant needed in the FQG theory. It looks promising that the FQG theory may offer a novel way to calculate all the free constants in the Standard Model of particle physics and general relativity.
Scientific Impotence of Seismology. Causes of Decline and Necessary Measures for the Revival of Earthquake Science

For most progressive scientists, it is no secret that the current state of seismology can be described as a science based not on the scientific principles of studying the natural phenomenon of earthquakes, but on the principles of fortune-telling on coffee grounds, dice, and tarot cards. Since the serious study of the causes of seismic phenomena began three hundred years ago [1], seismology has been stuck in that time and, by and large, has not advanced a single centimeter. I may be asked: - What about Mr. Reid's famous theory of earthquakes from 1910, Elastic Recoil [2], and many clones based on this theory? Have they not advanced the understanding of seismic processes to a level corresponding to the modern development of sciences in the world? Some scientists answered this question for me [3-7]: - Mr. Reid's theory is a vivid example of a false theory based on scientific incompetence of scientists, a model of brazen violation of the fundamental laws of science and the foundation of false and ignorant conclusions. This is a kind of “Elastic decoy” that has led more than one generation of seismologists into a scientific dead end, from which they cannot find a way out. In this case, another question arises: - Why did scientists not see and still do not see that Mr. Reid was a banal storyteller and conducted and conducted their research on the basis of his vicious “Elastic nonsense"? We will not reveal the big secret of the reason for such a terrible state of affairs in seismology if we say that it lies in the catastrophically low level of academic training of geophysical professors in all universities of the world, without exception, which stuffs students with its near-scientific geophysical junk, breeding ignoramuses like themselves in seismology, who in turn will cram their incompetence into their heads students. And it happens day after day and year after year. We understand that by asserting this, we offend most of the professors of seismology around the world, but it is impossible to continue to tolerate this state of affairs, because:”Amicus Plato, sed magis amica est veritas". In this article, we will try to answer the question: Why is this state of affairs in seismology and how do we defeat Mr. Reid's “Elastic Decoy”?
Black-box CoVaR and Its Gradient Estimation

CoVaR is an important measure of financial systemic risks. Due to the complex structures and dependence of portfolios, estimating the CoVaR of portfolio losses as well as its gradient information could be very challenging, especially in a black-box setting, where only noisy estimates of losses are available. For CoVaR and its gradient estimation, we propose a stochastic approximation algorithm that is applicable in various complicated cases, including black-box and streaming data scenarios. We establish the strong consistency and rate of convergence of the algorithm under appropriate technical conditions. The optimal rates of convergence of our proposed CoVaR and its gradient estimators are of orders $O(n^{-1/2})$ and $O(n^{-1/3})$, respectively, where $n$ denotes the number of algorithm iterations. Simulation experiments support our theoretical conclusions.
Digital Token Valuation: Looking for a New Gold Standard?

The economic valuation of digital tokens cannot ignore the valuation and financial effects of their underlying (historically represented by the “gold standard”). The consequence is that, despite various proposed approaches, the valuation of tokens with no underlying (such as bitcoins) and of tokens pegged to fiat money, precious metals, or cryptocurrencies is almost impossible. Alternatively, making economic valuations of digital tokens linked to decentralized finance projects that incorporate rights to financial returns is possible. However, the formulas must be adapted to the specific characteristics of decentralized finance. Investors can take an active or passive role and, therefore, have a different view of the expected return on their investment. In this chapter, we investigated even if this classification is a further discriminating factor in the choice of method of valuation.
Avaliação da Personalidade na Era do Big Data: Uma Revisão Sistemática

The range of naturalistic observations of personality in online environments has been expanded with new communication technologies. That made it possible to capture a large volume of digital footprints (Big Data). This research aimed to carry out a systematic literature review of national and international scientific articles that used digital footprints to observe personality in the period from 2011 to 2020. The articles were accessed in the electronic databases PsycINFO, SciELO, Scopus (Elsevier), and PePSIC. In total, 25 articles were found, the most used social networks to obtain digital traces were Facebook and Twitter. It has also been found that personality assessment using digital footprints has been used for different purposes, such as developing product recommendation systems and automatic cyberbullying detection systems. The importance of Brazilian researchers to acquire the technical resources necessary to conduct empirical research on personality and big data is discussed, considering that only one of the articles found was published by Brazilian researchers.Keywords: Personality, digital footprints, social networks, observation methods, Big Data
Decentralized Finance (DeFi)

Decentralized finance, often referred to as DeFi, is a blockchain-based financial system that aims to provide open and permissionless access to financial services and products without the need for intermediaries, such as banks or traditional financial institutions. DeFi leverages the decentralized nature of blockchain technology to create transparent, secure, and programmable financial applications that operate on public or permissionless networks. In DeFi, the replacement of intermediaries with programmable smart contracts does not allow the application of traditional theories of risk management and it is necessary to use a different approach. Creating an investment protocol in the blockchain is fascinating and would open access to the capital market to interesting projects that often find financing difficulties.
Cybersecurity

Cybersecurity refers to the practice of protecting computer systems, networks, and data from unauthorized access, theft, damage, disruption, or any other form of cyber threat. It involves implementing various measures, policies, technologies, and practices to ensure the confidentiality, integrity, and availability of information and systems.
Cryptocurrencies and Non-Fungible Tokens

Cryptocurrencies are digital or virtual currency whose main key features are the prevention of double-spending, the use of encryption to ensure confidentiality and confidentiality of transactions, the management of the computer network (the blockchain) on which coins are issued, and transactions are made by miners with the total absence of central authorities rendering the transactions theoretically immune to government interference or manipulation and the necessity for the members of the network of having to reach a diffused consent to proceed to a correct recording of the transactions and to render such definitive registrations in the ledger. While cryptocurrencies share general characteristics, there are numerous cryptocurrencies available, each with its unique features, purposes, and underlying technologies. The analysis of the dynamics that affect their value and of the evaluation methodologies also contribute to explaining the nature of the digital tokens that embed the cryptocurrencies.