Reviewerly/exHarmony huggingface dataset:
Modern Library Potential as Science Popularizer: Inquiry Results of SB RAS Scientists

The objective of the article is to present the results of a study of scientists’ attitude to communication between science and society by means of libraries. The working method of the study was an online questionnaire of academic specialists of the Siberian Branch of the Russian Academy of Sciences (SB RAS) research institutes. Data collection took place in early 2023. The questionnaire consisted of 20 questions, divided into four thematic blocks: 1) the main science popularizers in Russia (researchers’ opinion); 2) participation of SB RAS research institutes in communication with society and the most important directions of this activity; 3) attitude of scientists to necessity of interaction with society aimed at popularization of science; 4) attitude of scientists to libraries as science and society communication participants including science popularization. Basing on the data obtained, Siberian scientists appear to be, in general, positive about libraries as popularizers of domestic science and technology achievements. However, they consider scientists themselves and media the main actors in this field, as well as scientific societies, research and educational institutions, museums and professional science communicators. A number of respondents noted low attendance of libraries, their archaic and closed nature, and lack of sufficient competence of library specialists to promote science at a high level. Nevertheless, most of the researchers highly appreciated library as a full-fledged science and society communication participant. Many SB RAS scientists have experience of cooperation with libraries, such as, for example, reading popular science lectures and preparing exhibitions. The survey showed that researchers are not well aware of the activities and opportunities of modern libraries, therefore library institutions are invited to be active in building effective interaction with science and society.
Trends in the Development of Agricultural Research Institutions Scientists’ Demand for SibSAL Information Services

The object of the article is to present the results of the analysis of the demand for information services provided by the Siberian Scientific Agricultural Library, the branch of the State Public Scientific and Technological Library of the Siberian Branch of the Russian Academy of Sciences (SibSAL– the branch of SPSTL SB RAS), for scientists of agricultural research institutions (SRI) in the context of continuing organizational and structural reorganization. The start point of reformation was initiated by the adoption of the Federal Law “On the Russian Academy of Sciences, Reorganization of State Academies of Sciences and Amendments to Certain Legislative Acts of the Russian Federation” (September 27, 2013), which introduced fundamental changes in the organizational system of the branch of domestic science. Because of the termination of the activities of the Agricultural Academy, the departmental subordination of agricultural research institutes was fundamentally modified by transferring them to the existing and established federal research scientific centers of the Russian Academy of Sciences and regional branch centers. The ­result of the study revealed the trends of increasing demand for SibSAL services, confirmed that, despite the process of departmental reassignment, a steadily functioning library and information environment is a unifying and driving factor in conducting scientific research, stimulating the development and search for new forms and methods of information support.
Mikhail Zinovievich Vinokurov: Yakut Emigrant, Librarian at the USA Library of Congress

The purpose of this study was to reconstruct the personal history of Mikhail Zinovievich Vinokurov (1894-1983), one of the brightest representatives of the Russian intelligentsia of Yakutia, as a biographical narrative. The biography and heritage of M. Z. Vinokurov are considered in the historiography of the topic under study, publications of foreign and Russian researchers. The article reveals the American period of M. Vinokurov life in emigration, cites documents from his collection, transferred in 1983 to the State Library of Alaska (Alaska, USA), copies of which are stored in the National Library of the Republic of Sakha (Yakutia). M. Z. Vinokurov was a writer and public figure who devoted his whole life to enlightenment and creative activity for the benefit of his motherland. While in exile, being a librarian of the US Library of Congress, he initiated and implemented a global project to form the Russian collection of books, ethnographic sources of Siberia, manuscripts, photographs, letters related to Russian America, the Orthodox Church and the literary heritage of the Russian emigration. In this work, he saw his patriot mission. Introduction of Mikhail Vinokurov’s biographical world to the scientific context dictates the need to turn to new archival, visual and personal sources. The article also presents materials on the perpetuation of the memory of M. Z. Vinokurov by the National Library of the Republic of Sakha (Yakutia) in the form of publishing documents from his collection. The plan-prospect of the collection of documents ”The Heritage of Mikhail Zinovievich Vinokurov” in 4 volumes was prepared and approved. The c ollection will become an important step in preserving and promoting the rich cultural heritage of Yakutia and Russia as a whole, as well as in further studying the history of Russian intelligentsia and culture.
CMFS-Net: Common Mode Features Suppression Network for Gaze Estimation

Gaze estimation typically involves determining the direction or point of gaze based on a single image of the eye or face. However, due to variations in the internal structure and morphology of eyes among individuals, existing models for gaze estimation have limited accuracy. They often exhibit subject-dependent bias and a high degree of variance in their output. To address these limitations, calibration is commonly used to improve accuracy by mapping individuals' gaze predictions to real gaze. In this study, we propose an innovative approach for gaze estimation called the image common-mode feature suppression network. By training this network to suppress common-mode features, we can forecast the difference in gaze between two input images from the same subject. Furthermore, by leveraging the inferred differences and a set of calibration images for a specific object, we can forecast the gaze direction for a new eye sample. Our hypothesis is that by contrasting the two eye images, we can significantly reduce confounding factors that typically affect single-image forecasting methods, thereby producing superior predictions. To evaluate our approach, we curated a dataset specifically for training and testing the network. The experimental results demonstrate that our proposed model achieves gaze estimation with an error of less than 5.3 mm.
Mitigating Context Bias in Action Recognition via Skeleton-Dominated Two-Stream Network

In the realm of intelligent manufacturing and industrial upgrading, sophisticated multimedia computing technologies play a pivotal role in the recognition of video actions. However, most studies suffer from the issue of background bias, where the models excessively focus on the contextual information within the videos rather than concentrating on comprehending the human actions themselves. This could potentially lead to severe misjudgments in industrial applications. In this paper, we propose a Skeleton-Dominated Two-Stream Network (SDTSN), which is a novel two-stream framework that fuses and ensembles the skeleton and RGB modalities for video action recognition. Experimental results on the Mimetics dataset, without any background bias, demonstrate the efficacy of our approach.
Metaliteracy advocacy: The need for libraries to engage users in the Metaverse

The emergence of the metaverse is a notable trend in the field of librarianship that comes with it the capability to facilitate virtual interactions, stimulate engagement, and offer participatory experiences for both librarians and library users. This article delves into the potential applications of the metaverse within libraries. To explore this subject, the researchers conducted a systematic literature review, collecting secondary data that aligns with the study’s objectives. The findings emphasize the significance of developing metaliteracy among librarians and library users in the context of the metaverse. Metaliteracy encompasses a diverse set of skills that are essential for thriving in the digital age. Nevertheless, it also presents challenges, such as rapid technological advancements and disparities in accessibility. In response to these challenges, libraries and librarians must adapt by harnessing the power of metaliteracy to unlock the metaverse's potential fully. The article proposes that libraries should provide ongoing training and support for digital librarians and library users to ensure the relevance of their metaliteracy skills. Furthermore, libraries should actively foster collaborative partnerships with entities within the metaverse, including virtual museums and galleries. This collaborative approach enriches the pool of information and resources available to library users, ultimately enhancing their overall experience.
Intelligent System for Automation of Security Audits (SIAAS)

Events related to cybersecurity failures have a high and growing financial, operational, and reputational impact, on organizations around the world. At the same time, there is a shortage of cybersecurity professionals. In addition, the specialization of professionals with the necessary skills in the area of cybersecurity is expensive and time-consuming. Taking these facts into consideration, this research has focused on the automation of cybersecurity processes, specifically those related to continuous vulnerability detection. To address this problem, a cybersecurity vulnerability scanner that is free to the community and requires no pre-expertise on the part of the operator, was developed. The artifact was tested by companies in the IT business, by systems engineers, most without cybersecurity background. The results demonstrated that the artifact was easy to install and that the reported results can be used by the operator in the context of an automatic and proactive securitization of the systems involved.
Impact of Optical Connectors on QKD Transmission

This paper describes the effect of different optical connector polishing techniques on the quantum channel of a quantum key distribution (QKD) Clavis3 system. First, the basic differences among these types of connector polishing are briefly described. Second, the paper focuses on the design of a simple optical route to measure the effect of APC/UPC (angled physical contact/ultra physical contact) connectors on the quantum channel functionality, which is attributed to the slightly different characteristics of the two types of connectors. However, both types of connectors are commonly employed in real networks. For this reason, two series of measurements are made, one measurement for APC connectors and one measurement for UPC connectors. In each series, the number of connectors is gradually increased by up to 12 connectors per route for a given type. Finally, the results of the measurements, which mainly comprise changes in the three main parameters, are presented. In particular, the key rate, interferometric visibility and QBER (quantum bit error rate) were monitored.
DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.
rOpenSci News Digest, October 2023

Dear rOpenSci friends, it’s time for our monthly news roundup! You can read this post on our blog.Now let’s dive into the activity at and around rOpenSci!rOpenSci HQ Help us translate our dev guide to Portuguese Since last year we started the translation and localization of our Spanish version of our comprehensive guide to software development.
The refractive index of a single atom experienced by a single photon

The refractive index of a system is often viewed as the collective response of a medium to an electromagnetic field. Nevertheless, even if targeted at a single atom, the incoming light undergoes the dispersion. By studying the propagation of a single photon interacting with a two-level atom, we examine the dispersion behavior, calculate the phase and group velocity of the photon wave packet, and further analyze the dispersion experienced by the photon. These findings are critical for advancing long-distance quantum communications.
Accelerating the nonlinear analysis of hyper-elastic behavior by GMDH-assisted Newton-Raphson scheme

This paper presents an accelerated iterative scheme for nonlinear problems. Commonly, analysis of nonlinear behavior is conducted by the Newton-Raphson (NR) method. It is well-known that the number of iterations required depends on the deviation between the “starting point” and the converged solution. In practice, the solution of previous load step is taken as the “starting point”, while the converged solution of the current load step is not known beforehand. Therefore, difficulties or even non-convergence may occur. Recently, it is suggested that a neural network is employed to predict the solution of the current load step. This prediction is then used as the “starting point” for NR scheme. It is expected, that the true converged solution (of current step) is closer to the prediction by neural network than to the solution of previous load step. As a result, the scheme becomes faster due to less iterations. Obviously, any techniques for time-series forecasting can be used. Here, the Group Method of Data Handling (GMDH) is proposed. Loosely speaking, GMDH is a feedforward neural network without backpropagation. Practically, the GMDH-assisted NR scheme should not take longer time than conventional NR scheme. The advantage of GMDH is fast computation; however, the accuracy may be not as high as a network that has backpropagation. Therefore, careful consideration on the construction of GMDH network is needed. In the current work, the performance of GMDH-assisted NR scheme is investigated in analysis of hyper-elastic behavior, which involves both geometrical and material nonlinearity. A study on the influence of activation function on the accuracy is presented. Also, it is found that prediction for incremental displacement (between the current load step and the previous load step) could be better than prediction of displacement of the current load step.
Efficient concept drift handling for batch android malware detection models

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: (1) the frequency with which the models are retrained, and (2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.
A Co-design Study for Multi-stakeholder Job Recommender System Explanations

Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called ‘high-risk’ domains, such as recruitment. Within recruitment, AI has become commonplace, mainly in the form of job recommender systems (JRSs), which try to match candidates to vacancies, and vice versa. However, common XAI techniques often fall short in this domain due to the different levels and types of expertise of the individuals involved, making explanations difficult to generalize. To determine the explanation preferences of the different stakeholder types - candidates, recruiters, and companies - we created and validated a semi-structured interview guide. Using grounded theory, we structurally analyzed the results of these interviews and found that different stakeholder types indeed have strongly differing explanation preferences. Candidates indicated a preference for brief, textual explanations that allow them to quickly judge potential matches. On the other hand, hiring managers preferred visual graph-based explanations that provide a more technical and comprehensive overview at a glance. Recruiters found more exhaustive textual explanations preferable, as those provided them with more talking points to convince both parties of the match. Based on these findings, we describe guidelines on how to design an explanation interface that fulfills the requirements of all three stakeholder types. Furthermore, we provide the validated interview guide, which can assist future research in determining the explanation preferences of different stakeholder types.
Finding Spurious Correlations with Function-Semantic Contrast Analysis

Abstract In the field of Computer Vision (CV), the degree to which two objects, e.g. two classes, share a common conceptual meaning, known as semantic similarity, is closely linked to the visual resemblance of their physical appearances in the data: entities with higher semantic similarity, typically exhibit greater visual resemblance than entities with lower semantic similarity. Deep Neural Networks (DNNs) employed for classification exploit this visual similarity, incorporating it into the network’s representations (e.g., neurons), resulting in the functional similarity between the learned representations of visually akin classes, often manifesting in correlated activation patterns. However, such functional similarities can also emerge from spurious correlations — undesired auxiliary features that are shared between classes, such as backgrounds or specific artifacts. In this work, we present the Function-Semantic Contrast Analysis (FSCA) method, which identifies potential unintended correlations between network representations by examining the contrast between the functional distance of representations and the knowledge-based semantic distance between the concepts these representations were trained to recognize. While natural discrepancy is expected, our results indicate that these differences often originate from harmful spurious correlations in the data. We validate our approach by examining the presence of spurious correlations in widely-used CV architectures, demonstrating that FSCA offers a scalable solution for discovering previously undiscovered biases, that reduces the need for human supervision and is applicable across various Image Classification problems.
Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility

Counterfactual explanations are rising in popularity when aiming to increase the explainability of machine learning models. This type of explanation is straightforward to understand and provides actionable feedback (i.e., how to change the model decision). One of the main challenges that remains is generating meaningful counterfactuals that are coherent with real-world relations. Multiple approaches incorporating real-world relations have been proposed in the past, e.g. by utilizing data distributions or structural causal models. However, evaluating whether the explanations from different counterfactual approaches fulfill known causal relationships is still an open issue. To fill this gap, this work proposes two metrics - Semantic Meaningful Output (SMO) and Semantic Meaningful Relations (SMR) - to measure the ability of counterfactual generation approaches to depict real-world relations. In addition, we provide multiple datasets with known structural causal models and leverage them to benchmark the semantic meaningfulness of new and existing counterfactual approaches. Finally, we evaluate the semantic meaningfulness of nine well-established counterfactual explanation approaches and conclude that none of the non-causal approaches were able to create semantically meaningful counterfactuals consistently.