Reviewerly/exHarmony huggingface dataset:
Hegel in the Mirrors of Soviet Philosophy

The attitude to Hegel in Soviet philosophy was contradictory and depended to a considerable extent on ideological conjuncture. Waves of love and hate for Hegel alternated periodically. At different times emphasis was placed on the “revolutionary” method, dialectics, or on the “reactionary” system, the justification of the old world. On one page Lenin admired Hegel’s logical discoveries, on the next page he scolded him with harsh words for idealism, mysticism and “goddikin”. The article draws a parallel between the stylistics of Lenin’s philosophical works and the avant-garde artists and poets who gave a “slap in the face” to public taste. Philosophy becomes in Lenin’s hands the servant of politics; later on, the “principle of partisanship” became the credo of Marxist dogma and the criterion of the truth of philosophical doctrines. The Hegelian wing of Soviet Marxism was formed in the 1920s. The party leader of the “Dialecticians”, Abram Deborin, initiated the publication of Hegel’s Collected Works, which was published with a number of large and small interruptions over the course of 30 years (and the last, 15th volume never saw the light of day). After the defeat of the Deborinites in 1931, Hegel’s popularity gradually declined. However, some original studies coloured by love for Hegel appeared. Vygotsky used Hegelian concepts of “mediation” and “cunning of reason” to create a cultural-historical psychology; he believed that Hegel “walked lamely to the truth”. Mikhail Lifshits regarded Hegel as a “great conservative of mankind”, and Georg Lukács, who came to the Soviet Union, wrote his famous Young Hegel here and defended this book as his doctoral thesis (1942). Lifshits and Lukács concentrate on Hegel’s “historical dialectic” and on his comprehension of the revolutionary events of his epoch. By the end of the Great Patriotic War, Hegel’s philosophy had been declared an “aristocratic reaction to the French Revolution” (Stalin), and hatred of Hegel became reflexive. The party of persecutors of “Hegelianshchina” was led by Zinovy Beletsky, Professor at Moscow State University. It was only after Stalin’s death that serious research into Hegel’s philosophy could be resumed. E. V. Ilyenkov interpreted dialectics as “the method of ascent from the abstract to the concrete” and traced how this — materialistically reinterpreted — method works in Marx’s Das Kapital. It was Neo-Hegelians who brought to the fore the category of the concrete, understood as “diversity fused into unity” (Ivan Ilyin); in this respect, Lenin was in full solidarity with them. In parallel with European Marxists, Ilyenkov criticised the interpretation of dialectics as a universal picture of the world, a new metaphysics cultivated by “diamat” and “istmat”. He did not, however, share the anti-Hegelian sentiments of the schools of G. della Volpe and L. Althusser. For Ilyenkov, Hegel is the greatest revolutionary in logic since Aristotle. At the end of the article are the facts showing that interest in “Soviet Hegel” is still alive today.
A Living Classic of Library Science: Bio-bibliography of Yu.N. Stolyarov (2008—2023)

. In commemoration of the 85th anniversary of the birth of the outstanding Russian librarian Yu.N. Stolyarov, a bibliographic list of his works for 2008—2023 is published. It includes 500 titles (monographs, textbooks, composing works; articles, reports, reviews; foreign publications and translations; publications published under the scientific editorship of Yu.N. Stolyarov). The bibliographic list of publications about the scientist with the same chronological coverage includes 75 titles. In addition, a brief description of the special role of Yu.N. Stolyarov in the development of the theory and practice of librarianship, bibliography and book studies is given. This publication continues the bio-bibliographic index published by Nauka Publishing House in 2008 for the 70th anniversary of the scientist.
Fault-tolerant measurement-free quantum error correction with multiqubit gates

Measurement-free quantum error correction (MFQEC) offers an alternative to standard measurement-based QEC in platforms with an unconditional qubit reset gate. We revisit the question of fault tolerance (FT) for a measurement-free variant of the Steane code that leverages multiqubit gates and redundant syndrome extraction, finding previously overlooked phase-flip errors that undermine FT. We then construct a revised MFQEC circuit that is resistant to all single-qubit errors, but which nonetheless cannot tolerate certain correlated errors. In order to investigate FT systematically, we introduce an efficient method to classically simulate MFQEC circuits with (i) Clifford gates for syndrome extraction, (ii) syndrome-controlled Pauli operations for decoding, and (iii) a Pauli noise model. We thereby find a pseudothreshold of $\ensuremath{\sim}0.7%$ for our revised MFQEC Steane code under a restricted noise model previously considered in the literature. We then relax noise model assumptions to identify general requirements for FT with multiqubit gates, finding that existing multiqubit neutral atom gates are incompatible with fault-tolerant syndrome extraction in a straightforward implementation of both measurement-based and measurement-free variants of the Steane code. Decomposing multiqubit gates into two-qubit gates similarly spoils FT. Finally, we discuss the theoretical ingredients that are necessary to recover FT for MFQEC codes, including single-shot FT and a recent proposal by Heu\ss{}en et al. [arXiv:2307.13296] to achieve FT by ``copying'' errors onto an ancilla register. By combining multiqubit gates, redundant syndrome extraction, and copy-assisted FT, we construct a measurement-free and fault-tolerant variant of the Steane code with a pseudothreshold of $\ensuremath{\sim}0.1%$.
The African Continental Free Trade Area and Financial Development for Female Labour Force Participation in Africa

The study assesses how financial development dynamics can moderate the incidence of African trade integration on female labour force participation. The focus is on 47 African countries for the period 1995 to 2019 and the empirical evidence is based on Fixed Effects regressions. The findings show that financial development moderates African trade integration to engender an overall positive effect on female labour force participation. Moreover, financial depth proxied by liquid liabilities should reach a threshold of approximately 14.304 (% of GDP) in order to completely dampen an initial negative incidence of intra-African trade integration on female labor force participation. It follows that financial development becomes a necessary and sufficient condition to moderate intra-African trade integration in order to positively affect female labor force participation only when the established threshold of financial depth is attained. Other policy implications are discussed.
An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment

In practical applications, learning models that can perform well even when the data distribution is different from the training set are essential and meaningful. Such problems are often referred to as out-of-distribution (OOD) generalization problems. In this paper, we propose a method for OOD generalization based on causal inference. Unlike the prevalent OOD generalization methods, our approach does not require the environment labels associated with the data in the training set. We analyze the causes of distributional shifts in data from a causal modeling perspective and then propose a backdoor adjustment method based on variational inference. Finally, we constructed a unique network structure to simulate the variational inference process. The proposed variational backdoor adjustment (VBA) framework can be combined with any mainstream backbone network. In addition to theoretical derivation, we conduct experiments on different datasets to demonstrate that our method performs well in prediction accuracy and generalization gaps. Furthermore, by comparing the VBA framework with other mainstream OOD methods, we show that VBA performs better than mainstream methods.
Accountability in permissioned blockchains: through the ledger, the code and the people

Purpose This study explores how introducing a permissioned blockchain in a supply chain context impacts accountability relationships and the process of rendering an account. The authors explore how implementing a digital transformation impacts the governance of network transactions. Design/methodology/approach The authors mobilize 28 interviews and documentary analysis. The authors focus on early blockchain adopters to get an insight into how implementing a permissioned blockchain can transform information sharing, coordination and collaboration between business partners, now converted into network participants. Findings The authors suggest that implementing a permissioned blockchain impacts accountability across three levers, namely through the ledger, through the code and through the people, where these levers are interconnected. Blockchains are often valued for their ability to enable transparency through the visibility of transactions, but the authors argue that this is an incomplete view. Rather, transparency alone does not help to satisfy a duty of accountability, as it can result in selective disclosure or obfuscation. Originality/value The authors extend the conceptualizations of accountability in the blockchain literature by focusing on how accountability relationships are enacted, and accounts are rendered in a permissioned blockchain context. Additionally, the authors complement existing work on accountability and governance by suggesting an integrated model across three dimensions: ledger, code and people.
Reconstructing Image Composition: Computation of Leading Lines

The composition of an image is a critical element chosen by the author to construct an image that conveys a narrative and related emotions. Other key elements include framing, lighting, and colors. Assessing classical and simple composition rules in an image, such as the well-known "rule of thirds", has proven effective in evaluating the aesthetic quality of an image. It is widely acknowledged that composition is emphasized by the presence of leading lines. While these leading lines may not be explicitly visible in the image, they connect key points within the image and can also serve as boundaries between different areas of the image. For instance, the boundary between the sky and the ground can be considered a leading line in the image. Making the image's composition explicit through a set of leading lines is valuable when analyzing an image or assisting in photography. To the best of our knowledge, no computational method has been proposed to trace image leading lines. We conducted user studies to assess the agreement among image experts when requesting them to draw leading lines on images. According to these studies, which demonstrate that experts concur in identifying leading lines, this paper introduces a fully automatic computational method for recovering the leading lines that underlie the image's composition. Our method consists of two steps: firstly, based on feature detection, potential weighted leading lines are established; secondly, these weighted leading lines are grouped to generate the leading lines of the image. We evaluate our method through both subjective and objective studies, and we propose an objective metric to compare two sets of leading lines.
An Improved Generative Adversarial Network with Feature Filtering for Imbalanced Data

Article An Improved Generative Adversarial Network with Feature Filtering for Imbalanced Data Jun Dou 1, and Yan Song 2,* 1 Department of Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, China 2 Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China * Correspondence: sonya@usst.edu.cn;Tel.:+86-21-55271299; fax:+86-21-55271299 Received: 7 October 2023 Accepted: 31 October 2023 Published: 21 December 2023 Abstract: Generative adversarial network (GAN) is an overwhelming yet promising method to address the data imbalance problem. However, most existing GANs that are usually inspired by computer vision techniques have not yet taken the significance and redundancy of features into consideration delicately, probably producing rough samples with overlapping and incorrectness. To address this problem, a novel GAN called improved GAN with feature filtering (IGAN-FF) is proposed, which establishes a new loss function for the model training by replacing the traditional Euclidean distance with the Mahalanobis distance and taking the ℓ1,2-norm regularization term into consideration. The remarkable merits of the proposed IGAN-FF can be highlighted as follows: 1) the utilization of the Mahalanobis distance can make a fair evaluation of different attributes without neglecting any trivial/small-scale but significant ones. In addition, it can mitigate the disturbance caused by the correlation between features; 2) the embedding of ℓ1,2-norm regularization term into the loss function contributes greatly to the feature filtering by guaranteeing the data sparsity as well as helps reduce risk of overfitting. Finally, empirical experiments on 16 well-known imbalanced datasets demonstrate that our proposed IGAN-FF performs better at most evaluation metrics than the other 11 state-of-the-art methods.
Knowledge-Based and Generative-AI-Driven Pedagogical Conversational Agents: A Comparative Study of Grice’s Cooperative Principles and Trust

The emergence of generative language models (GLMs), such as OpenAI’s ChatGPT, is changing the way we communicate with computers and has a major impact on the educational landscape. While GLMs have great potential to support education, their use is not unproblematic, as they suffer from hallucinations and misinformation. In this paper, we investigate how a very limited amount of domain-specific data, from lecture slides and transcripts, can be used to build knowledge-based and generative educational chatbots. We found that knowledge-based chatbots allow full control over the system’s response but lack the verbosity and flexibility of GLMs. The answers provided by GLMs are more trustworthy and offer greater flexibility, but their correctness cannot be guaranteed. Adapting GLMs to domain-specific data trades flexibility for correctness.
Elementary Number Theory Problems. Part IX

Summary This paper continues the formalization of chosen problems defined in the book “250 Problems in Elementary Number Theory” by Wacław Sierpiński.
PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning

Abstract Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P ‐waves and 0.12 s for S ‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments.
Improved Square-Root Cubature Kalman Filtering Algorithm for Nonlinear Systems with Dual Unknown Inputs

For nonlinear discrete systems with dual unknown inputs, there are many limitations regarding previous nonlinear filters. This paper proposes two new, improved square-root cubature Kalman filtering (ISRCKF) algorithms to estimate system states and dual unknown inputs. Improved square-root cubature Kalman filtering 1 (ISRCKF1) introduces an innovation that first obtains the unknown input estimates from the measurement equation, then updates the innovation to derive the unknown input estimates from the state equation, then uses the already obtained estimates of the dual unknown inputs to correct the one-step estimate of the state, and finally the minimum variance unbiased estimate of the state is obtained. Improved square-root cubature Kalman filtering 2 (ISRCKF2) builds a unified innovation feedback model, then applies the minimum variance unbiased estimation (MVUE) criterion to obtain the estimates of system states and dual unknown inputs, refining a more concise recursive filter but requiring stronger assumptions. Finally, simulation results demonstrate that the above two algorithms can achieve the optimal estimates of system states and dual unknown inputs simultaneously, and ISRCKF2 further enhances the accuracy of both state and dual unknown inputs estimation, which verifies the validity of the proposed algorithms.
Exemplary Ethereum Development Strategies Regarding Security and Gas-Saving

Ethereum is a rapidly evolving blockchain with new features as well as new vulnerabilities being introduced regularly. Interaction with the network is costly compared to other blockchains or traditional software systems. When starting to develop on Ethereum, a supported smart contract programming language needs to be learned, most notably Solidity. Having various pitfalls raises the question of what the best practices for the safe and efficient usage of Ethereum are. This study primarily aims to combine knowledge from existing research resources, while also introducing new approaches learned from practical smart contract development analysis and inquiry, which are subsequently compiled into lists of best practices. The most important findings are that code quality and security should be prioritized. Moreover, some simple gas-saving strategies can help to decrease interaction costs with little effort.
Comment on “Multiparty quantum mutual information: An alternative definition”

We show that, contrary to the claim by Kumar [Phys. Rev. A 96, 012332 (2017)], the quantum dual total correlation of an $n$-partite quantum state cannot be represented as the quantum relative entropy between $n-1$ copies of the quantum state and the product of $n$ different reduced quantum states for $n \geq 3$. Specifically, we argue that the latter fails to yield a finite value for generalized $n$-partite Greenberger-Horne-Zeilinger states.
A Matrix Coding-Oriented Reversible Data Hiding Scheme Using Dual Digital Images

With the development of Internet technology, information security and data protection have become particularly important. Reversible data hiding is an effective technique for data integrity and privacy protection, and secret image sharing is a distinct research field within reversible data hiding. Due to the ability of sharing secret information between two receivers and the larger embedding capacity compared to the traditional reversible data hiding scheme, dual digital images have also attracted extensive research in the past decade. In this paper, we propose a reversible data hiding scheme based on matrix coding using dual digital images. By modifying the bits in the pixels, we can conceal three bits of the secret message in two pixels. In other words, the embedding rate reaches 1.5 bits per pixel (bpp). The experimental results demonstrate that our method has a significantly larger embedding capacity of 786,432 bits compared to previous similar methods while still maintaining acceptable image quality defined by a peak signal-to-noise ratio (PSNR) greater than 30 dB. The proposed scheme is suitable for applications required to pass a large amount of data but with minor security of image quality to be visually acceptable.
Ped-Mix: Mix Pedestrians for Occluded Person Re-identification

Occluded person re-identification is a very challenging task due to the interference of occluding objects. Most existing approaches concentrate on modifying the network architecture to facilitate the extraction of more distinctive local features or render the network less sensitive to occlusions. However, it is easy to fail when encountering previously unseen occlusions or when other humans act as occluders, due to the limited occlusion variance in the training set. In this paper, we propose a data augmentation method that blends the target pedestrian with other pedestrians to simulate non-target pedestrian occlusion. Furthermore, we propose a non-target suppression (NTS) loss to reduce the information flow from the occluded region to the final embedding, where the occluded region can be easily obtained from the augmentation. Experimental results demonstrate that this simple augmentation technique yields significant performance improvements in the task of occluded person re-identification.
Research on Obstacle Avoidance Planning for UUV Based on A3C Algorithm

Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning.