Reviewerly/exHarmony huggingface dataset:
Тraditional values and book culture

The draft Presidential Decree on the fundamentals of state policy for the preservation and strengthening of traditional Russian spiritual and moral values, put out by Likhachev Russian Research Institute for Cultural and Natural Heritage in January 2022, for public consideration, was analyzed. Based on the ancient cult of the book as a sacred revelation, the book culture is understood as a creative and communication activity of human society carried out in the book form through symbols. In the history of Russian civilization, the book culture has acquired the status of traditional national value and productive force that ensures the development of material, spiritual and humanistic culture. The technocratic society of the twentieth century is at risk of self-destruction. Risks such as environmental crisis (environmental pollution, climate change), biological mutations (viral pandemic, genetic degradation), technological accidents (computer program failure, nuclear facility collapse), geopolitical follies (arms race, terrorism, aggressive globalization, loss of national identity and sovereignty, genocide, fanaticism), and many other anthropogenic threats have acquired global proportions. Successful opposition to dangerous technogenic risks is possible only if we rely on the humanistic book and library traditions. The conclusion is made on the expediency of the need to develop a scientifically and practically sound state strategy for humanistic book culture in modern Russia.
The service-based model of educational organization’s library

Theoretical, regulative and organizational foundations for developing e-libraries are discussed along with the experience of their operation in educational systems in Russia and worldwide. The author defines the digital (electronic) library of an educational institution as the integrated organizational, software and technological tools to support educational activities, computerize key library and information processes, integrated at the level of user data with library and information services within the e-libraries and public libraries, digital educational resources and platforms. When building the model of digital (electronic) library, educational organizations are recommended to apply service-based approach so to structurize the list of services of information acquisition, processing, storage, representation and delivery to library users, e. g. students, pedagogues, parents and administrators. The minimum requirements, including these used in the related information systems, to data within the service-based model are determined; the repertory of analytical digital services for assessing, forecasting and modelling of educational activities, is suggested. These approaches are efficient in digital transformation of libraries and building of digital (electronic) libraries within the structure of educational environment.
The very beginning (on the 75-th anniversary of Yulia P. Melentyeva)

The article is published to celebrate the jubilee of Yulia P. Melentyeva. The author examines the stages of her life and professional career, beginning from her study at Moscow Library College and Moscow State Institute of Culture. The factors that influenced the choice of library profession are mentioned. Melentyeva's contribution to the national library science is emphasized.
A note on the relation between the Contextual Fraction and CNT2

Contextuality (or lack thereof) is a property of systems of random variables. Among the measures of the degree of contextuality, two have played important roles. One of them, Contextual Fraction ($\text{CNTF}$) was proposed within the framework of the sheaf-theoretic approach to contextuality, and extended to arbitrary systems in the Contextuality-by-Default approach. The other, denoted $\text{CNT}_{2}$, was proposed as one of the measures within the Contextuality-by-Default approach. In this note, I prove that $\text{CNTF}=2\text{CNT}_{2}$ within a class of systems, called cyclic, that have played a prominent role in contextuality research.
Accuracy assessment of smart devices for Geoscience field mapping

We present the measurement of fractures near the town of Beaufort West, South Africa. A field visit was conducted to examine the dip and azimuth of rock outcrops in and around the town. The locations of these various fractures were mapped and their orientation, which included the dip and strike of the rock surface, was measured using a geological compass (i.e., Brunton Truarc 15 Compass). The geological compass measurements were then compared to three mobile devices. These mobile devices, namely an iPad 2 and two smartphones (Samsung S8 and Huawei P10 Lite), all had the same application for standardization and the mobile device results were individually compared to the geological compass. The data stemming from the various mobile devices and the geological compass were then compared in terms of their variance. This statistical analysis was performed using the Correlated T-test method, as well as the Pearson Correlation Coefficient formula. To visually examine the main fracture orientations, the data obtained using the geological compass was plotted on a rose diagram. Results show that the relationship between the geological compass and the mobile device readings had little to no correlation, when using both the correlation and t-tests as combined determinants. This highlights the importance of ensuring measurement accuracy in the field as well as instrument calibration in situ.
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS

Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to reproduce similar high quality when a whole paragraph is considered in TTS, where a large amount of contextual information needs to be considered in building a paragraph-based TTS model. To alleviate the difficulty in training, we propose to model linguistic and prosodic information by considering cross-sentence, embedded structure in training. Three sub-modules, including linguistics-aware, prosody-aware and sentence-position networks, are trained together with a modified Tacotron2. Specifically, to learn the information embedded in a paragraph and the relations among the corresponding component sentences, we utilize linguistics-aware and prosody-aware networks. The information in a paragraph is captured by encoders and the inter-sentence information in a paragraph is learned with multi-head attention mechanisms. The relative sentence position in a paragraph is explicitly exploited by a sentence-position network. Trained on a storytelling audio-book corpus (4.08 hours), recorded by a female Mandarin Chinese speaker, the proposed TTS model demonstrates that it can produce rather natural and good-quality speech paragraph-wise. The cross-sentence contextual information, such as break and prosodic variations between consecutive sentences, can be better predicted and rendered than the sentence-based model. Tested on paragraph texts, of which the lengths are similar to, longer than, or much longer than the typical paragraph length of the training data, the TTS speech produced by the new model is consistently preferred over the sentence-based model in subjective tests and confirmed in objective measures.
Graph Perceiver IO: A General Architecture for Graph Structured Data

Multimodal machine learning has been widely studied for the development of general intelligence. Recently, the remarkable multimodal algorithms, the Perceiver and Perceiver IO, show competitive results for diverse dataset domains and tasks. However, recent works, Perceiver and Perceiver IO, have focused on heterogeneous modalities, including image, text, and speech, and there are few research works for graph structured datasets. A graph is one of the most generalized dataset structures, and we can represent the other dataset, including images, text, and speech, as graph structured data. A graph has an adjacency matrix different from other dataset domains such as text and image, and it is not trivial to handle the topological information, relational information, and canonical positional information. In this study, we provide a Graph Perceiver IO, the Perceiver IO for the graph structured dataset. We keep the main structure of the Graph Perceiver IO as the Perceiver IO because the Perceiver IO already handles the diverse dataset well, except for the graph structured dataset. The Graph Perceiver IO is a general method, and it can handle diverse datasets such as graph structured data as well as text and images. Comparing the graph neural networks, the Graph Perceiver IO requires a lower complexity, and it can incorporate the local and global information efficiently. We show that Graph Perceiver IO shows competitive results for diverse graph-related tasks, including node classification, graph classification, and link prediction.
DMCA: Dense Multi-agent Navigation using Attention and Communication

In decentralized multi-robot navigation, the agents lack the world knowledge to make safe and (near-)optimal plans reliably and make their decisions on their neighbors' observable states. We present a reinforcement learning based multi-agent navigation algorithm that performs inter-agent communications. In order to deal with the variable number of neighbors for each agent, we use a multi-head self-attention mechanism to encode neighbor information and create a fixed-length observation vector. We pose communication selection as a link prediction problem, where the network predicts whether communication is necessary given the observable information. The communicated information augments the observed neighbor information and is used to select a suitable navigation plan. We highlight the benefits of our approach by performing safe and efficient navigation among multiple robots in dense and challenging benchmarks. We also compare the performance with other learning-based methods and highlight improvements in terms of fewer collisions and time-to-goal in dense scenarios.
Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles

Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and for decision making. The linear approximation and so-called likelihood profiles are well-known possibilities for the calculation of confidence and prediction intervals for nonlinear regression models. These simple and effective techniques have been completely ignored so far in the genetic programming literature. In this work we describe the calculation of likelihood profiles in details and also provide some illustrative examples with models created with three different symbolic regression algorithms on two different datasets. The examples highlight the importance of the likelihood profiles to understand the limitations of symbolic regression models and to help the user taking an informed post-prediction decision.
Finding Common Ground for Incoherent Horn Expressions

Autonomous systems that operate in a shared environment with people need to be able to follow the rules of the society they occupy. While laws are unique for one society, different people and institutions may use different rules to guide their conduct. We study the problem of reaching a common ground among possibly incoherent rules of conduct. We formally define a notion of common ground and discuss the main properties of this notion. Then, we identify three sufficient conditions on the class of Horn expressions for which common grounds are guaranteed to exist. We provide a polynomial time algorithm that computes common grounds, under these conditions. We also show that if any of the three conditions is removed then common grounds for the resulting (larger) class may not exist.
Inputs, Outputs, and Composition in the Logic of Information Flows

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this paper is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs, and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded-variable FO.
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models

As pre-trained language models become more resource-demanding, the inequality between resource-rich languages such as English and resource-scarce languages is worsening. This can be attributed to the fact that the amount of available training data in each language follows the power-law distribution, and most of the languages belong to the long tail of the distribution. Some research areas attempt to mitigate this problem. For example, in cross-lingual transfer learning and multilingual training, the goal is to benefit long-tail languages via the knowledge acquired from resource-rich languages. Although being successful, existing work has mainly focused on experimenting on as many languages as possible. As a result, targeted in-depth analysis is mostly absent. In this study, we focus on a single low-resource language and perform extensive evaluation and probing experiments using cross-lingual post-training (XPT). To make the transfer scenario challenging, we choose Korean as the target language, as it is a language isolate and thus shares almost no typology with English. Results show that XPT not only outperforms or performs on par with monolingual models trained with orders of magnitudes more data but also is highly efficient in the transfer process.
Federated Learning based on Defending Against Data Poisoning Attacks in IoT

The rapidly expanding number of Internet of Things (IoT) devices is generating huge quantities of data, but the data privacy and security exposure in IoT devices, especially in the automatic driving system. Federated learning (FL) is a paradigm that addresses data privacy, security, access rights, and access to heterogeneous message issues by integrating a global model based on distributed nodes. However, data poisoning attacks on FL can undermine the benefits, destroying the global model's availability and disrupting model training. To avoid the above issues, we build up a hierarchical defense data poisoning (HDDP) system framework to defend against data poisoning attacks in FL, which monitors each local model of individual nodes via abnormal detection to remove the malicious clients. Whether the poisoning defense server has a trusted test dataset, we design the \underline{l}ocal \underline{m}odel \underline{t}est \underline{v}oting (LMTV) and \underline{k}ullback-\underline{l}eibler divergence \underline{a}nomaly parameters \underline{d}etection (KLAD) algorithms to defend against label-flipping poisoning attacks. Specifically, the trusted test dataset is utilized to obtain the evaluation results for each classification to recognize the malicious clients in LMTV. More importantly, we adopt the kullback leibler divergence to measure the similarity between local models without the trusted test dataset in KLAD. Finally, through extensive evaluations and against the various label-flipping poisoning attacks, LMTV and KLAD algorithms could achieve the $100\%$ and $40\%$ to $85\%$ successful defense ratios under different detection situations.
Some criteria for integer sequences pair being realizable by a graph

Let $A=(a_1,\ldots,a_n)$ and $B=(b_1,\ldots,b_n)$ be two sequences of nonnegative integers with $a_i \le b_i$ for $1\le i\le n$. The pair $(A;B)$ is said to be realizable by a graph if there exists a simple graph $G$ with vertices $v_1,\ldots, v_n$ such that $a_i\le d_G(v_i)\le b_i$ for $1\le i\le n$. Let $\preceq$ denote the lexicographic ordering on $Z\times Z:$ $(a_{i+1},b_{i+1})\preceq (a_i,b_i)\Longleftrightarrow [(a_{i+1}<a_i)\vee ((a_{i+1}=a_i)\&(b_{i+1}\le b_i))]$. We say that the sequences $A$ and $B$ are in good order if $(a_{i+1},b_{i+1})\preceq (a_i,b_i)$. In this paper, we consider the generalizations of six classical characterizations on sequences pair due to Berge, Ryser et al. and present related results.
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups

Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models developed on one dataset may not generalize across diverse subpopulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to explore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data.
Reproducibility-Oriented and Privacy-Preserving Genomic Dataset Sharing

As genomic research has become increasingly widespread in recent years, few studies share datasets due to the sensitivity in privacy of genomic records. This hinders the reproduction and validation of research outcomes, which are crucial for catching errors (e.g., miscalculations) during the research process.To the best of our knowledge, we are the first to propose a method of sharing genomic datasets in a privacy-preserving manner for GWAS outcome reproducibility.In this work, we introduce a differential privacy-based scheme for sharing genomic datasets to enhance the reproducibility of genome-wide association studies (GWAS) outcomes. The scheme involves two stages. In the first stage, we generate a noisy copy of the target dataset by applying the XOR mechanism on the binarized (encoded) dataset, where the binary noise generation considers biological features. However, the initial step introduces significant noise, making the dataset less suitable for direct GWAS validation. Thus, in the second stage, we implement a post-processing technique that adjusts the Minor Allele Frequency (MAF) values in the noisy dataset to align more closely with those in a publicly available dataset using optimal transport and decode it back to genomic space. We evaluated the proposed scheme on three real-life genomic datasets and compared it with a baseline approach and two synthesis-based solutions with regard to detecting errors of GWAS outcomes, data utility, and resistance against membership inference attacks (MIAs). Our scheme outperforms all the comparing methods in detecting GWAS outcome errors, achieves better utility and provides higher privacy protection against membership inference attacks (MIAs). By utilizing our method, genomic researchers will be inclined to share a differentially private, yet of high quality version of their datasets.
Tensor Rank and Other Multipartite Entanglement Measures of Graph States

Graph states play an important role in quantum information theory through their connection to measurement-based computing and error correction. Prior work has revealed elegant connections between the graph structure of these states and their multipartite entanglement content. We continue this line of investigation by identifying additional entanglement properties for certain types of graph states. From the perspective of tensor theory, we tighten both upper and lower bounds on the tensor rank of odd ring states ($|R_{2n+1}\rangle$) to read $2^n+1 \leq rank(|R_{2n+1}\rangle) \leq 3*2^{n-1}$. Next, we show that several multipartite extensions of bipartite entanglement measures are dichotomous for graph states based on the connectivity of the corresponding graph. Lastly, we give a simple graph rule for computing the n-tangle $\tau_n$.
Self-supervised Multi-Modal Video Forgery Attack Detection

Video forgery attack threatens the surveillance system by replacing the video captures with unrealistic synthesis, which can be powered by the latest augment reality and virtual reality technologies. From the machine perception aspect, visual objects often have RF signatures that are naturally synchronized with them during recording. In contrast to video captures, the RF signatures are more difficult to attack given their concealed and ubiquitous nature. In this work, we investigate multimodal video forgery attack detection methods using both vision and wireless modalities. Since wireless signal-based human perception is environmentally sensitive, we propose a self-supervised training strategy to enable the system to work without external annotation and thus can adapt to different environments. Our method achieves a perfect human detection accuracy and a high forgery attack detection accuracy of 94.38% which is comparable with supervised methods.
Graph-constrained Analysis for Multivariate Functional Data

Functional Gaussian graphical models (GGM) used for analyzing multivariate functional data customarily estimate an unknown graphical model representing the conditional relationships between the functional variables. However, in many applications of multivariate functional data, the graph is known and existing functional GGM methods cannot preserve a given graphical constraint. In this manuscript, we demonstrate how to conduct multivariate functional analysis that exactly conforms to a given inter-variable graph. We first show the equivalence between partially separable functional GGM and graphical Gaussian processes (GP), proposed originally for constructing optimal covariance functions for multivariate spatial data that retain the conditional independence relations in a given graphical model. The theoretical connection help design a new algorithm that leverages Dempster's covariance selection to calculate the maximum likelihood estimate of the covariance function for multivariate functional data under graphical constraints. We also show that the finite term truncation of functional GGM basis expansion used in practice is equivalent to a low-rank graphical GP, which is known to oversmooth marginal distributions. To remedy this, we extend our algorithm to better preserve marginal distributions while still respecting the graph and retaining computational scalability. The insights obtained from the new results presented in this manuscript will help practitioners better understand the relationship between these graphical models and in deciding on the appropriate method for their specific multivariate data analysis task. The benefits of the proposed algorithms are illustrated using empirical experiments and an application to functional modeling of neuroimaging data using the connectivity graph among regions of the brain.