Reviewerly/exHarmony huggingface dataset:
Legal Regulation of Qualification Requirements for the Teaching Staff of Higher Educational Institutions

The subject of the study is the legal regulation of qualification requirements for the teaching staff of higher educational institutions in the Russian Federation. The authors discuss the main qualification requirements that apply to the positions of assistant, teacher, senior lecturer, associate professor, professor. Universities may introduce additional requirements when announcing a competition for positions, in most cases they are reduced to the applicant's compliance with a certain level of publication activity. The legal regulation of the requirements for the teaching staff of higher educational institutions is carried out in accordance with the norms of the Labor Code, the Law "On Education in the Russian Federation", the corresponding Order of the Ministry of Health and Social Development of the Russian Federation. The activity of the teaching staff is connected with the implementation of federal state educational programs, which also prescribe the requirements for personnel. As a result of the study, the specifics of the qualification requirements for scientific and pedagogical workers were revealed. The complexity of the professional functions performed by the teaching staff largely determines the existence of a fairly extensive legal framework regulating social and labor relations between an employee and an employer. The analysis of this legal framework indicates not only its multi-step nature and complexity, but also a certain instability. A number of qualification requirements have recently undergone significant changes. A certain obstacle to starting a teaching profession is the qualification requirements for the position of an assistant. The requirements for the teaching staff vary not only from position to position, but also vary depending on the institution of higher education.
Bayesian nonparametric mixtures of Exponential Random Graph Models for ensembles of networks

Ensembles of networks arise in various fields where multiple independent networks are observed, for example, a collection of student networks from different classes. However, there are few models that describe both the variations and characteristics of networks in an ensemble at the same time. In this manuscript, we propose to model ensembles of networks using a Dirichlet Process Mixture of Exponential Random Graph Models (DPM-ERGMs), which divides an ensemble into different clusters and models each cluster of networks using a separate Exponential Random Graph Model (ERGM). By employing a Dirichlet process mixture, the number of clusters can be determined automatically and changed adaptively with the data provided. Moreover, in order to perform full Bayesian inference for DPM-ERGMs, we develop a Metropolis-within-slice sampling algorithm to address the problem of sampling from the intractable ERGMs on an infinite sample space. We also demonstrate the performance of DPM-ERGMs with both simulated and real datasets.
qaAskeR$$^+$$: a novel testing method for question answering software via asking recursive questions

Question Answering (QA) is an attractive and challenging area in NLP community. With the development of QA technique, plenty of QA software has been applied in daily human life to provide convenient access of information retrieval. To investigate the performance of QA software, many benchmark datasets have been constructed to provide various test cases. However, current QA software is mainly tested in a reference-based paradigm, in which the expected outputs (labels) of test cases are mandatory to be annotated with much human effort before testing. As a result, neither the just-in-time test during usage nor the extensible test on massive unlabeled real-life data is feasible, which keeps the current testing of QA software from being flexible and sufficient. In this work, we propose a novel testing method, qaAskeR $$^+$$ , with five new Metamorphic Relations for QA software. qaAskeR $$^+$$ does not refer to the annotated labels of test cases. Instead, based on the idea that a correct answer should imply a piece of reliable knowledge that always conforms with any other correct answer, qaAskeR $$^+$$ tests QA software by inspecting its behaviors on multiple recursively asked questions that are relevant to the same or some further enriched knowledge. Experimental results show that qaAskeR $$^+$$ can reveal quite a few violations that indicate actual answering issues on various mainstream QA software without using any pre-annotated labels.
OSAP‐Loss: Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

Abstract In existing remote sensing image retrieval (RSIR) datasets, the number of images among different classes varies dramatically, which leads to a severe class imbalance problem. Some studies propose to train the model with the ranking‐based metric (e.g., average precision [AP]), because AP is robust to class imbalance. However, current AP‐based methods overlook an important issue: only optimising samples ranking before each positive sample, which is limited by the definition of AP and is prone to local optimum. To achieve global optimisation of AP, a novel method, namely Optimising Samples after positive ones &amp; AP loss (OSAP‐Loss) is proposed in this study. Specifically, a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper bound. Then, a novel loss called Optimising Samples after Positive ones (OSP) loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each sample. Finally, a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP optimisation. Extensive experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.
Productivity and semantic transparency

Abstract We used word embeddings to study the relation between productivity and semantic transparency. We compiled a dataset with around 2700 two-syllable compounds that shared position-specific constituents (henceforth pivots) and some 1100 suffixed words. For each pivot and suffix, we calculated measures of productivity as well as measures of semantic transparency. For compounds, productivity ( P ) was negatively correlated with the number of types ( V ) and with the semantic similarity between non-pivot constituents and their compounds. Conversely, the greater semantic similarity of the pivot with either the compound or the non-pivot constituent predicted higher degrees of productivity. Visualization with t-SNE revealed clustering of suffixed words’ embeddings, but no by-pivot clustering for compounds, except for a minority of pivots whose regions in semantic space did not contain intruding unrelated compounds. A subset of these pivots was found to realize a fixed shift in semantic space from the base word to the corresponding compound, a property that also emerged for several suffixes. For these pivots, no correlation between P and V was present. Thus, Mandarin compounds appear to realize, at one extreme, motivated but unsystematic concept formation (where other pivots could just as well have been used), and at the other extreme, systematic suffix-like semantics.
Computing education pathways from high school to community college: key strategies for aligning institutional systems

Background and context High school students’ interest in computing fields is not always sustained in community college due to a disconnect between institutions.Objective To understand how cross-sector collaborations can align institutional pathways in computing. Research questions: What cross-sector practices can be used to build a computing education pathway? What practices can be used to promote student agency? How does a cross-sector partnership address issues of equity in the computing education pathway?Method A case study of a cross-sector research-practice partnership using interviews, observations, and meeting documentation.Findings Key strategies for aligning institutional systems to increase persistence in computing classes include building cross-sector connections and structures that support student agency, and addressing the structural factors that perpetuate inequityImplications The findings can inform efforts to build college and career pathways across the transition from high school to community college.
Combining Neural Networks with Logic Rules

How to utilize symbolic knowledge in deep learning is an important problem. Deep neural networks are flexible and powerful, while symbolic knowledge has the virtue of interpretability and intuitiveness. It is necessary to combine the two together to inject symbolic knowledge into neural networks. We propose a novel approach to combine neural networks with logic rules. In this approach, task-specific supervised learning and policy-based reinforcement learning are performed alternately to train a neural model until convergence. The basic idea is to use supervised learning to train a deep model and use reinforcement learning to propel the deep model to meet logic rules. In the process of the policy gradient reinforcement learning, if a predicted output of a deep model meets all logical rules, the deep model is given a positive reward, otherwise, it is given a negative reward. By maximizing the expected rewards, the deep model can be gradually adjusted to meet logical constraints. We conduct experiments on the tasks of named entity recognition. The experimental results demonstrate the effectiveness of our method.
Resource Allocation in Decentralized Vehicular Edge Computing Network

Computation-intensive vehicle tasks sharply increase with the rapid growth of intelligent vehicles. The technology of Mobile Edge Computing (MEC) has the possibility of assisting vehicles with computation offloading. To solve the problem of computation resource management and guarantee the security of resource transaction, we jointly combine the MEC network and the blockchain networks to build a blockchain based MEC offloading model. The non-cooperative interactions between MEC server and vehicles formulate a two-stage Stackelberg game in an aim to maximize their benefits and information security. We theoretically demonstrate the unique existence of Nash equilibrium, which enables participants to decide their optimal strategies. Finally, the performance of the proposed model is analyzed by conducting simulation experiments. Our proposed model optimizes resource allocation and also improves the security of the whole network.
Modelling the control subsystem in the management system of the region’s mineral resource complex

The aim of the study is to form a control subsystem model in the management system of the region’s mineral resource complex. The article is devoted to solving the main task to form a flow diagram for operating the control subsystem in the management system of the region’s mineral resource complex for monitoring indicators of subprogram 1 “Reproduction of the mineral resource base, geological study of the subsoil” within the framework of the Russian Federation state programme “Reproduction and use of natural resources”. The concepts of the set-theoretic approach and the method of mathematical analysis are used in the paper. The work features are forming an algorithm for operating the control subsystem in the management system of the region’s mineral resource complex, using mathematical tools. As a result of the study, the proposed model of the control subsystem in the management system of the region’s mineral resource complex is tested on the example of statistical data on reserves for five types of solid minerals. The adequacy of the obtained model of the control subsystem in the management system of the region’s mineral resource complex is confirmed by the calculations carried out in the work. The simulated control subsystem can be replicated for other types of minerals.
Assessing Information Search by Task Outcome

People do not search information as such but seek to get a job done or to manage a situation by the help of search results. Therefore, the ultimate goal of information search is to advance task performance. It should be evaluated accordingly, i.e. by its contribution to task outcome. This implies an extended notion of search process, which also covers the use of information in search results for task outcome. For measuring the effect of search to task outcome we propose both indirect and direct indicators which measure search success.
Smartphone Usage in Science Education: A Systematic Literature Review

This article presents a review of research on smartphone usage in educational science settings published between January 2015 and August 2022, and aims to provide an overview of the constructs evaluated and to identify potential gaps in current research for researchers working on this topic. Specifically, the search for publications in the relevant years was narrowed down to such studies that provided empirical evidence for the impact of smartphone usage on teaching and learning in natural science education. The databases used for the search were ERIC, Scopus, and Web of Science. In total, 100 articles were surveyed. The study findings were categorized regarding the type of smartphone usage, as well as the type of educational institution and constructs investigated. Overall, the results from this review show that smartphone usage in educational science environments has the potential for rather positive effects, such as an increase in learning achievements or an increase in motivation, and smartphone usage rarely leads to detrimental effects. Despite the substantial amount of studies to date, more research in these areas would allow for more generalized statistical results and analyses and is therefore desirable.
Seeing the wood for the trees: predictive margins for random forests

Abstract Classification trees and random forests offer a number of attractive features to corpus data analysts. However, the way in which these models are typically reported – a decision tree and/or set of variable importance scores – offers insufficient information if interest centers on the (form of) relationship between (multiple) predictors and the outcome. This paper develops predictive margins as an interpretative approach to ensemble techniques such as random forests. These are model summaries in the form of adjusted predictions, which provide a clearer picture of patterns in the data and allow us to query a model on potential nonlinear associations and interactions among predictor variables. The present paper outlines the general strategy for forming predictive margins and addresses methodological issues from an explicitly (corpus) linguistic perspective. For illustration, we use data on the English genitive alternation and provide an R package and code for their implementation.
Building YoloV4 models for identification of rock minerals in thin section

Abstract Rock mineral identification is a costly and time-consuming task using conventional methods of testing physical and chemical properties, especially in the petrographic laboratory. A comprehensive identification model for three rock minerals in sedimentary rocks based on the YoloV4 model is available as a solution. The models predict rock minerals by calculating the pixels and the weights that have been trained previously. First, the YoloV4 models and framework were built. Then, a total of 44 manually labelled thin section images (sedimentary rocks thin section) were used to create the model to detect minerals accurately. The MAP and loss results showed that the parameters of the minerals detection model in PPL are 11% and 1.19, respectively. Meanwhile, The MAP and loss results of XPL are 19% and 1.18, respectively. Finally, Identification of rock minerals using deep learning algorithms is a very promising idea especially the YoloV4 model can build a comprehensive detection of rock samples in thin sections effectively.
Time-resolved laser-induced fluorescence spectroscopy and chemometrics for fast identification of U(VI)-bearing minerals in a mining context

We evaluated the potential of time-resolved laser-induced fluorescence spectroscopy (TRLFS) combined with chemometric methods for fast identification of U(VI)-bearing minerals in a mining context. We analyzed a sample set which was representative of several environmental conditions. The set consisted of 80 uranium-bearing samples related to mining operations, including natural minerals, minerals with uranium sorbed on the surface, and synthetic phases prepared and characterized specifically for this study. The TRLF spectra were processed using the Ward algorithm and the K-nearest neighbors (KNN) method to reveal similarities between samples and to rapidly identify the uranium-bearing phase and the associated mineralogical family. The predictive models were validated on an independent dataset, and then applied to test samples mostly taken from U mill tailings. Identification results were found to be in accordance with the available characterization data from X-ray diffraction (XRD) and scanning electron microscopy-energy dispersive X-ray spectrometry (SEM-EDX). This work shows that TRLFS can be an effective decision-making tool for environmental investigations or geological prospection, considering the large diversity of uranium-bearing mineral phases and their low concentration in environmental samples.
Cluster Partitions Optimization Using the Latent Class Analysis Technique

The paper explores the formation of a cluster partition using a system of bundles of vectors. Each object is represented by a vector with coordinates corresponding to its characteristic factors. The study focuses on the principles of changes in the total density of these bundles when objects move from one cluster to another. Based on the findings, an algorithm is proposed to reduce the total intracluster variance of the initial cluster partition.&#x0D; The proposed algorithm is based on a modern technique for analyzing latent classes. This technique emphasizes that the forming factors of objects within each cluster should be highly correlated. Since the number of forming factors is arbitrary, the algorithm replaces this requirement with the maximum possible proximity of object factor vectors to the average vector within each cluster. The degree of this closeness is referred to as the tightness of the cluster vector bundle.&#x0D; The paper also introduces a new method for quantifying clusters in the improvable clustering constructed using the proposed algorithm. A practical example of the algorithm's application to process medical data is presented. The study discusses the reasons for the dependence of the algorithm's outcome on the choice of initial cluster partition.
A Bayesian Semisupervised Approach to Keyword Extraction with Only Positive and Unlabeled Data

In the era of big data, people benefit from the existence of tremendous amounts of information. However, availability of said information may pose great challenges. For instance, one big challenge is how to extract useful yet succinct information in an automated fashion. As one of the first few efforts, keyword extraction methods summarize an article by identifying a list of keywords. Many existing keyword extraction methods focus on the unsupervised setting, with all keywords assumed unknown. In reality, a (small) subset of the keywords may be available for a particular article. To use such information, we propose a rigorous probabilistic model based on a semisupervised setup. Our method incorporates the graph-based information of an article into a Bayesian framework via an informative prior so that our model facilitates formal statistical inference, which is often absent from existing methods. To overcome the difficulty arising from high-dimensional posterior sampling, we develop two Markov chain Monte Carlo algorithms based on Gibbs samplers and compare their performance using benchmark data. We use a false discovery rate (FDR)-based approach for selecting the number of keywords, whereas the existing methods use ad hoc threshold values. Our numerical results show that the proposed method compared favorably with state-of-the-art methods for keyword extraction. History: Accepted by Ramaswamy Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1283 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0234 ) at ( http://dx.doi.org/10.5281/zenodo.7348935 ).
Generative adversarial networks based motion learning towards robotic calligraphy synthesis

Abstract Robot calligraphy visually reflects the motion capability of robotic manipulators. While traditional researches mainly focus on image generation and the writing of simple calligraphic strokes or characters, this article presents a generative adversarial network (GAN)‐based motion learning method for robotic calligraphy synthesis (Gan2CS) that can enhance the efficiency in writing complex calligraphy words and reproducing classic calligraphy works. The key technologies in the proposed approach include: (1) adopting the GAN to learn the motion parameters from the robot writing operation; (2) converting the learnt motion data into the style font and realising the transition from static calligraphy images to dynamic writing demonstration; (3) reproducing high‐precision calligraphy works by synthesising the writing motion data hierarchically. In this study, the motion trajectories of sample calligraphy images are firstly extracted and converted into the robot module. The robot performs the writing with motion planning, and the writing motion parameters of calligraphy strokes are learnt with GANs. Then the motion data of basic strokes is synthesised based on the hierarchical process of ‘stroke‐radical‐part‐character’. And the robot re‐writes the synthesised characters whose similarity with the original calligraphy characters is evaluated. Regular calligraphy characters have been tested in the experiments for method validation and the results validated that the robot can actualise the robotic calligraphy synthesis of writing motion data with GAN.
Computational infrastructure for concepts discovery in science and technology

Automated scientific discovery, a topic in artificial intelligence has mainly been used to generate scientific insight from data. Our work follows the knowledge-driven discovery approach and introduces the use of category theory as the foundation for modeling diverse engineering fields represented with combinatorial representation. We show how category theory provides support for all stages of the discovery process starting from modeling the engineering knowledge. We demonstrate the use of the approach to rediscover previous discoveries in mechanics and discover new devices, some of which need to be realized to be appreciated. Category theory allows expanding the process to disciplines not modeled with combinatorial representations. We intend to demonstrate this in future studies.
Robotics Innovation for the Elderly [From the Editor’s Desk]

As I write this editorial, it is the beginning of 2023. I would like to wish you and your loved ones a happy and healthy new year. We hope that 2023 will be a better year. Year 2022 was difficult for those in the war zone and those who struggled with policies on the COVID-19 pandemic. While the pandemic is at the back of most people’s minds in many countries, as China abruptly abandoned its zero-COVID policy, hospitals in China became overcrowded very quickly, and the health-care system has faced a major stress test since December 2022. Sadly, among the most vulnerable populations, older adults were hit badly and could not get needed help in time, which led to many deaths. We need to take care of the elderly and work on robotics innovations to assist them.