Reviewerly/exHarmony huggingface dataset:
Applicability of online professional education to the conditions of the Russian Arctic zone: the position of educational institutions

Introduction. Ensuring the staffing needs of the economies of the Arctic regions is one of the main tasks of educational institutions of higher and secondary professional education located in the subjects of the Arctic zone of the Russian Federation (hereinafter referred to as the "AZ RF"). However, the labor markets of the regions of the AZ RF experience a staff shortage, and the local staff training system does not have sufficient capacity. The rapid development of information and communication technologies and the restriction on people's mobility due to the pandemic create the conditions for a more active use of distance learning in order to compensate for the acute staff shortage. In this regard, the issue of studying the possibilities of using the online form of training of professionals for the economies of the Arctic regions becomes relevant from the position of the educational institutions that are well acquainted with the educational process from the inside. Materials and methods. The information base of the study included the results of surveys of 362 educational institutions of higher education (hereinafter referred to as "HE") and secondary professional education (hereinafter referred to as "SPE") out of 397 ones located in regions that are territorially partially or completely part of the AZ RF. When processing the survey results, the methods of system analysis, descriptive statistics and comparative data analysis were used. Research results. About 70% of universities are in favor of the use of online education in professional training and about 65% of educational institutions of SPE are against it. The opinions of educational institutions of HE and SPE on a possible transition to distance learning in a number of specialties were similarly distributed: 60% – “for” and 65% – “against”, respectively. When answering the question about the readiness of employers of the AZ RF to hire specialists with a diploma of education in an online form, educational institutions of HE and SPE were unanimous: about 30% were in favor, about 45% were against, and 25% found it difficult to answer. Conclusion. The results of the surveys showed a diametrically opposite attitude of educational institutions of HE and SPE to the applicability of online technologies to professional training. Representatives of educational institutions of HE are optimistic about the possibility of switching to an online form of education, while representatives of educational institutions of SPE are extremely skeptical. This can be explained by the high requirements of educational standards for a developed infrastructure and sufficient number of material and technical resources in educational institutions of SPE necessary for practical training, which is virtually impossible with an online form of education.
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-based tasks such as node classification and graph classification. However, many recent works have demonstrated that an attacker can mislead GNN models by slightly perturbing the graph structure. Existing attacks to GNNs are either under the less practical threat model where the attacker is assumed to access the GNN model parameters, or under the practical black-box threat model but consider perturbing node features that are shown to be not enough effective. In this paper, we aim to bridge this gap and consider black-box attacks to GNNs with structure perturbation as well as with theoretical guarantees. We propose to address this challenge through bandit techniques. Specifically, we formulate our attack as an online optimization with bandit feedback. This original problem is essentially NP-hard due to the fact that perturbing the graph structure is a binary optimization problem. We then propose an online attack based on bandit optimization which is proven to be {sublinear} to the query number $T$, i.e., $\mathcal{O}(\sqrt{N}T^{3/4})$ where $N$ is the number of nodes in the graph. Finally, we evaluate our proposed attack by conducting experiments over multiple datasets and GNN models. The experimental results on various citation graphs and image graphs show that our attack is both effective and efficient. Source code is available at~\url{https://github.com/Metaoblivion/Bandit_GNN_Attack}
A heuristic to determine the initial gravitational constant of the GSA

The Gravitational Search Algorithm (GSA) is an optimization algorithm based on Newton's laws of gravity and dynamics. Introduced in 2009, the GSA already has several versions and applications. However, its performance depends on the values of its parameters, which are determined empirically. Hence, its generality is compromised, because the parameters that are suitable for a particular application are not necessarily suitable for another. This paper proposes the Gravitational Search Algorithm with Normalized Gravitational Constant (GSA-NGC), which defines a new heuristic to determine the initial gravitational constant of the GSA. The new heuristic is grounded in the Brans-Dicke theory of gravitation and takes into consideration the multiple dimensions of the search space of the application. It aims to improve the final solution and reduce the number of iterations and premature convergences of the GSA. The GSA-NGC is validated experimentally, proving to be suitable for various applications and improving significantly the generality, performance, and efficiency of the GSA.
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learning

The goal of open-world compositional zero-shot learning (OW-CZSL) is to recognize compositions of state and objects in images, given only a subset of them during training and no prior on the unseen compositions. In this setting, models operate on a huge output space, containing all possible state-object compositions. While previous works tackle the problem by learning embeddings for the compositions jointly, here we revisit a simple CZSL baseline and predict the primitives, i.e. states and objects, independently. To ensure that the model develops primitive-specific features, we equip the state and object classifiers with separate, non-linear feature extractors. Moreover, we estimate the feasibility of each composition through external knowledge, using this prior to remove unfeasible compositions from the output space. Finally, we propose a new setting, i.e. CZSL under partial supervision (pCZSL), where either only objects or state labels are available during training, and we can use our prior to estimate the missing labels. Our model, Knowledge-Guided Simple Primitives (KG-SP), achieves state of the art in both OW-CZSL and pCZSL, surpassing most recent competitors even when coupled with semi-supervised learning techniques. Code available at: https://github.com/ExplainableML/KG-SP.
CLASSIFICATION SUPPORT VECTOR MACHINE IN BREAST CANCER PATIENTS

Support vector machine is one of the supervised learning methods in machine learning that is used in classification. The purpose of this study is to measure the accuracy of classification by using 3 hyperplane functions in SVM, namely linear, sigmoid, polynomial, and radial basis function (RBF). Based on the simulation results of training data and testing data on female breast cancer patients, SVM with hyperplane RBF has better accuracy than hyperplane polynomial, linear and sigmoid. The RBF results for the training and testing data were 89.1% and 73.2%, respectively. Based on the results of the classification of training data for female breast cancer patients, 88.07% had no recurrence and 93.33% had recurrence events. Meanwhile, based on the results of the classification of testing data, female patients did not recurrence events and recurrence events was 72.55% and 80.00%, respectively. So from this article, it can be concluded that SVM with hyperplane RBF is one of the best methods in the application of the method of classifying female breast cancer patients.
Contemporary music genre rhythm generation with machine learning

In this article, we present research on customizing a variational autoencoder (VAE) neural network to learn models and play with musical rhythms encoded within a latent space. The system uses a data structure that is capable of encoding rhythms in simple and compound meter and can learn models from little training data. To facilitate the exploration of models, we implemented a visualizer that relies on the dynamic nature of the pulsing rhythmic patterns. To test our system in real-life musical practice, we collected small-scale datasets of contemporary music genre rhythms and trained models with them. We found that the non-linearities of the learned latent spaces coupled with tactile interfaces to interact with the models were very expressive and lead to unexpected places in composition and live performance musical settings. A music album was recorded and it was premiered at a major music festival using the VAE latent space on stage.
Rain Station Network Analysis in the Sampean Watershed: Comparison of Variations in Data Aggregation

The lack of rainfall-runoff accuracy is important for some applications. The choice of data aggregation that affects the estimation results is important at the level of accuracy. Some commonly used aggregations are daily, ten days, and monthly rainfall. This study aimed to compare the results of the estimation of the effect of data aggregation and to analyze the density of the rain gauge network in the Sampean watershed. The evaluation of the rain station network is carried out through the Kagan calculation. Rainfall data are from the rainfall data records for 20 years at 33 rain gauge stations. Measurement of the performance of aggregation variations using the relationship between the correlation value of rainfall with the distance between station locations. Station network positioning is assessed from alignment errors and interpolation errors. The results showed differences in the correlation and estimation values ​​in the variation of data aggregation.The greater interval can increase the effectiveness of deployment with minimum error. Based on Kagan's analysis, there is an uneven distribution of gauge stations in the Sampean watershed eventhough the average and interpolation error in the monthly rainfall is less than 5%. It is this inequality that causes gauge stations to be inefficient.&#x0D; Keywords : Rain gauge network; correlation; Kagan; data aggregation&#x0D; Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember&#x0D; This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
What to Do before, during, and after an Experiment

Chapter 5 delves into the steps that occur prior to, during, and after an experiment – including arriving at questions to explore with an experiment; documenting the steps in the process of conducting an experiment; and considering whether to replicate one’s findings after an experiment. This discussion touches on the themes of the aforementioned open science movement, offering in many instances a cautionary perspective.
Schmidt rank and algebraic closure

Let $ {\mathbf k} $ be a field and $Q\in {\mathbf k}[x_1, \ldots, x_s]$ a form (homogeneous polynomial) of degree $d>1.$ The ${\mathbf k}$-Schmidt rank $rk_{\mathbf k}(Q)$ of $Q$ is the minimal $r$ such that $Q= \sum_{i=1}^r R_iS_i$ with $R_i, S_i \in {\mathbf k}[x_1, \ldots, x_s]$ forms of degree $<d$. When $ {\mathbf k} $ is algebraically closed, this rank is essentially equivalent to the codimension in $ {\mathbf k}^s $ of the singular locus of the variety defined by $ Q, $ known also as the Birch rank of $ Q. $ When $ {\mathbf k} $ is a number field, a finite field or a function field, we give polynomial bounds for $ rk_{\mathbf k}(Q) $ in terms of $ rk_{\bar {\mathbf k}} (Q) $ where $ \bar {\mathbf k} $ is the algebraic closure of $ {\mathbf k}. $ Prior to this work no such bound (even ineffective) was known for $d>4$. This result has immediate consequences for counting integer points (when $ {\mathbf k} $ is a number field) or prime points (when $ {\mathbf k} = \mathbb Q $) of the variety $ \{Q=0\} $ assuming $ rk_{\mathbf k} (Q) $ is large.
Analysis of The Vehicle Routing Problem Solved via Hybrid Quantum Algorithms in Presence of Noisy Channels

The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global solution. Quantum computing and quantum machine learning provide a new approach to solving combinatorial optimization of problems faster due to inherent speedups of quantum effects. Many solutions of VRP are offered across different quantum computing platforms using hybrid algorithms such as quantum approximate optimization algorithm and quadratic unconstrained binary optimization. In this work, we build a basic VRP solver for 3 and 4 cities using the variational quantum eigensolver on a fixed ansatz. The work is further extended to evaluate the robustness of the solution in several examples of noisy quantum channels. We find that the performance of the quantum algorithm depends heavily on what noise model is used. In general, noise is detrimental, but not equally so among different noise sources.
A Mobile-based Expert System for Disease Diagnosis Child using Best-First Search Algorithm

Currently, many parents want their children to be free from disease. Although this cannot be fully expected. Problems that often occur to parents are when their child is sick, lack of knowledge and limited sources of information about the disease that causes parents to leave their children without first aid. In other conditions, in areas that are far from the doctor's practice, the need for information on disease management is very necessary. Based on the problems that have been described previously, this expert system was created to assist parents in understanding the symptoms of skin diseases that occur in children. In the development of this expert system using the Best-First Search (BFS) algorithm as an inference engine. In this expert system application the user can choose the symptoms of the disease in children, then the output produced is the conclusion of the disease. From the test results based on Blackbox, it was found that 100% functionality runs according to the list of system requirements. After this research was completed, it was concluded that to design an expert system in detecting childhood diseases, starting from conducting interviews, followed by system design, the next process was implementing the system, then testing by experts for compatibility with the data that had been obtained.
REALIZATIONS OF MODALITY IN TEACHER-TO-TEACHER TALK OF FORCED ONLINE LEARNING

The objective of this study was to describe the ways the modality was realized in teacher-to-teacher talk of forced online learning. The data of this study were sentences expressing modality uttered by teachers while they were talking about forced online learning. Participant observation technique was performed to collect the data. Based on the data analysis conducted it was found that modality was realized through modal operators (47%), modal adjuncts (27%), interpersonal metaphors (16%), and predicator constituents (10%). The use of modal operators which was mostly found in realizing the modality indicates that the teachers tend to talk and think about states of affairs that are not present in the current situation and may never occur in the natural world. Moreover, there are some combinations of the realizations realized, namely: modal adjuncts and interpersonal metaphors; modal operators and predicator constituents; modal operators and modal adjuncts; interpersonal metaphors and predicator constituents; modal operators and interpersonal metaphors; modal operators, modal adjuncts, and interpersonal metaphors; and modal operators, interpersonal metaphors, and predicator constituents. It shows that teachers use some ways to assert or deny and to prescribe or proscribe when they express their judgments on forced online learning especially in informal situations.
Finite-State Semi-Markov Channels for Nanopore Sequencing

Nanopore sequencing is an emerging DNA sequencing technology that has been proposed for use in DNA storage systems. We propose the noisy nanopore channel model for nanopore sequencing. This model captures duplications, inter-symbol interference, and noisy measurements by concatenating an i.i.d. duplication channel with a finite-state semi-Markov channel. Compared to previous models, this channel models the dominant distortions of the nanopore while remaining tractable. Anticipating future coding schemes, we derive MAP detection algorithms and estimate achievable rates. Given that finite-state semi-Markov channels are a subclass of channels with memory, we conjecture that the achievable rate of the noisy nanopore channel can be optimised using a variation of the generalised Blahut-Arimoto algorithm.
Ginger Product Price Information Based on Semantic Heterogeneity of Multisource Network Information

In order to improve prices of ginger products information service, promote the transmission and interaction between prices of ginger products information data, agricultural products are roughly divided into nine categories. Through network research, the price information of agricultural products on the Internet is analyzed. According to the hierarchical theory of information processing, three types of semantic heterogeneity of schema, contextual data, and individual anomaly data with hierarchical progressive relationship are proposed. The results show that almost 100% of the main attributes exist in the network information sources, in addition, more than 80% of the websites have type differentiation of agricultural products, and less is provided for attributes such as specification and origin, which also have an impact on the price of agricultural products. Through the network information source, the attribute characteristics of multisource network prices of ginger products information are grasped, and the classification characteristics of existing semantic heterogeneity are combined, and it provides a strong support for constructing the classification system of semantic heterogeneity of prices of ginger products information.
Ecology of Media Space as a Strategy of Modern Life. Book Review: Urazova, S.L. (Ed.). (2021). Ecosystem of Media: Digital Modifications. Chelyabinsk: SUSU Publ. 252 р. (In Russ.)

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The bayesvl R package has been upgraded to version 1.0

This short communication piece is to mark the upgrading of the bayesvl R package to version 1.0. Its official version on The Comprehensive R Archive Network (frequently referred to as “CRAN”) was released on May 24, 2019, or bayesvl 0.8.5. The software development process can be traced back to its Github repository.
Gamified Speaker Comparison by Listening

We address speaker comparison by listening in a game-like environment, hypothesized to make the task more motivating for naive listeners. We present the same 30 trials selected with the help of an x-vector speaker recognition system from VoxCeleb to a total of 150 crowdworkers recruited through Amazon's Mechanical Turk. They are divided into cohorts of 50, each using one of three alternative interface designs: (i) a traditional (nongamified) design; (ii) a gamified design with feedback on decisions, along with points, game level indications, and possibility for interface customization; (iii) another gamified design with an additional constraint of maximum of 5 'lives' consumed by wrong answers. We analyze the impact of these interface designs to listener error rates (both misses and false alarms), probability calibration, time of quitting, along with survey questionnaire. The results indicate improved performance from (i) to (ii) and (iii), particularly in terms of balancing the two types of detection errors.
Generating Literal and Implied Subquestions to Fact-check Complex Claims

Verifying complex political claims is a challenging task, especially when politicians use various tactics to subtly misrepresent the facts. Automatic fact-checking systems fall short here, and their predictions like "half-true" are not very useful in isolation, since we have no idea which parts of the claim are true and which are not. In this work, we focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present ClaimDecomp, a dataset of decompositions for over 1000 claims. Given a claim and its verification paragraph written by fact-checkers, our trained annotators write subquestions covering both explicit propositions of the original claim and its implicit facets, such as asking about additional political context that changes our view of the claim's veracity. We study whether state-of-the-art models can generate such subquestions, showing that these models generate reasonable questions to ask, but predicting the comprehensive set of subquestions from the original claim without evidence remains challenging. We further show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers, suggesting that they can be useful pieces of a fact-checking pipeline.
AppTek's Submission to the IWSLT 2022 Isometric Spoken Language Translation Task

To participate in the Isometric Spoken Language Translation Task of the IWSLT 2022 evaluation, constrained condition, AppTek developed neural Transformer-based systems for English-to-German with various mechanisms of length control, ranging from source-side and target-side pseudo-tokens to encoding of remaining length in characters that replaces positional encoding. We further increased translation length compliance by sentence-level selection of length-compliant hypotheses from different system variants, as well as rescoring of N-best candidates from a single system. Length-compliant back-translated and forward-translated synthetic data, as well as other parallel data variants derived from the original MuST-C training corpus were important for a good quality/desired length trade-off. Our experimental results show that length compliance levels above 90% can be reached while minimizing losses in MT quality as measured in BERT and BLEU scores.