Reviewerly/exHarmony huggingface dataset:
Optimizing Vocational Education Design with Big Data and Educational Technology

Amidst the rapid advancements in big data and internet technologies, profound transformations have been witnessed in modern societal and economic structures. Vocational education, a pivotal component within the educational domain, faces challenges in course design and optimization. While the majority of existing research focuses on utilizing educational technology to optimize courses, most methodologies still rely on traditional data analysis and mining techniques, with little exploration of deep learning and big data technologies. This research proposes a novel strategy for course design and optimization. Initially, the course theme optimization is addressed through a short text clustering algorithm that utilizes a linear fusion of the biterm topic model (BTM) and global vectors for word representation (GloVe) similarity. Subsequently, course content is planned using a deep interest network (DIN) recommendation algorithm in conjunction with the gated recurrent unit (GRU) time series model. This study provides the vocational education sector with a new perspective on course design and optimization, which has important theoretical and practical implications.
CONSTRUCTION AND EVALUATION OF KNOWLEDGE MANAGEMENT SYSTEMS FOR GOVERNMENT DECISION-MAKING

Taking the construction of traditional information services in the scientific and technological information industry to knowledge services and management systems for government decision-making (especially strategic decision-making) as an example, this paper proposes regarding the content requirements and functional requirements of the government decision-making knowledge management system, explore the evaluation methods of the system, To promote the application of knowledge management ideas in government decision-making.
MODELLING THE PROCESSES OF IMPROVING THE INSTITUTIONAL SUPPORT FOR THE FORMATION AND IMPLEMENTATION OF THE STATE YOUTH POLICY

The article is devoted to the development and scientific substantiation of the components of the conceptual model of institutional support for the formation and implementation of the State youth policy. It is shown that young people are the most dynamic, mobile and promising segment of the population. It is young people who shape the future of the nation, and they have always been and remain in the focus of attention of public authorities, local self-government bodies, and civil society. The increased attention to youth in public administration is transformed into comprehensive and targeted actions to support youth development and make a positive contribution to society, which is possible on the basis of the formation and implementation of an effective state youth policy. The features and priorities for the implementation of youth policy in territorial communities are identified, in particular: creating an appropriate organisational and functional environment for local self-government bodies; harmonising normative, organisational, programme and target and other documents regulating the activities of local self-government bodies with similar higher-level documents; improving the effectiveness of information support for the implementation of youth policy through the promotion of the results of youth programmes and projects. It is shown that interagency and intersectoral cooperation between different ministries in the context of the development of institutional support for the formation and implementation of state youth policy remains an important factor for coordinated actions to achieve the expected results in the shortest possible time. The directions and content of interagency cooperation in the implementation of youth policy by central executive authorities are determined. The article builds a conceptual model of institutional support for the formation and implementation of the State youth policy, which presents its main systemic components (components) and justifies the expediency of their inclusion in the context. This model is descriptive in nature and is based on the legal, intellectual, creative, communicative, strategic and other aspects of the formation and implementation of the State youth policy, on the understanding of the needs, values and interests of young people, as well as on the ability to implement innovative, flexible and adaptive approaches to involve young people in socio-political processes.
Introduction to the Special Issue: Resources for Undergraduate Cryptology

This editorial introduces the special issue, Resources for Undergraduate Cryptology. We begin by describing possible roles for cryptology in the undergraduate mathematics curriculum together with a brief overview of the subject. We conclude with a brief preview of each paper included in this issue.
"Guitar Thinking" and "Genre Thinking" among an Online Community of Guitarists

Researchers and performers have long intuited that popular music’s organizational features are linked to the physical-tonal properties of the electric guitar. Yet current evidence is either too broad or too specific to support reliable generalizations. Ongoing corpus studies offer remarkable explanatory power in clarifying stylistic trends from a listener’s perspective but are not currently equipped to address the kinesthetic aspects of instrument performance.&#x0D; In this study I employ statistical methods of corpus analysis to explore how popular-music guitarists navigate the fretboard. I do so via a digital collection of guitar tablature: 257 rhythm-guitar parts sampled from seven decades of songs (1954–2019). An analysis of fretboard transitions reveals a highly idiomatic fretboard profile that aligns best with the minor pentatonic scale. This empirically supports standing intuitions about the relationship between pentatonicism and guitar performance, while underscoring the methodological and potential ethical shortcomings of a strictly harmonic perspective on popular music’s organization.
Assessment of the potential for restoration of the environmental state of the natural ecosystems disturbed by development of geo-resources based on satellite data

The factors of environmental state restoration of the natural ecosystems disturbed as the result of mining mineral deposits were investigated using the case study of the phytocenosis formed on the enclosing dam of the apatite-nepheline ore processing waste storage in the Khibiny group of deposits according to the technology developed in the Mining Institute of the Kola Scientific Center of the Russian Academy of Sciences for the restoration of disturbed lands through creation of a biologically active environment without deposition of a fertile layer. Based on the data of satellite observations of the monitoring sites during the vegetation period, the impact of relief as well as the heat and moisture availability on the state of the emerging vegetation cover was studied using gradient transects of different exposures along the height of the enclosing dam slope. The spatial resolution of the satellite images was harmonized using the enhanced pan-sharpening procedure with the minimum characteristic size of the dam slope elements and the representativeness of the data obtained when assessing the vegetation cover condition using the vegetation index and the moisture stress index was ensured. Generalization was made of the relationship between the parameters characterizing the vegetation cover state and abiotic factors of its formation using a neural network model. Two artificial neural networks were trained to predict the vegetation and the plant moisture stress indices using the data set obtained from processing of visible, infrared and thermal spectral channels of satellite images of the monitoring sites. The neural network model shows that the vegetation index of the emerging vegetation cover is antibate to the plant moisture stress index - the dominant factor in restoration of the environmental state of the investigated site, and to the surface temperature of the enclosing dam of the tailing dump. Dependence of the predicted moisture stress of the phytocenosis formed ontop the enclosing dam slope on the elevation and exposure was used to assess the potential of its restoration to the forest succession stage corresponding to the phytocenosis of the surrounding natural environment. Zoning of the tailing dump according to the potential for restoration of the environmental state was performed in order to support the decision-making process when planning environmental protection measures.
Deep Long Term Prediction for Semantic Segmentation in Autonomous Driving

Temporal prediction is an important function in autonomous driving (AD) systems as it forecasts how the environment will change and transform in the next few seconds. Humans have an inherited prediction capability that extrapolates a present scenario to the future. In this paper, we present a novel approach to look further into the future using a standard semantic segmentation representation and time series networks of varying architectures. An important property of our approach is its flexibility to predict an arbitrary time horizon into the future. We perform prediction in the semantic segmentation domain where inputs are semantic segmentation masks. We present extensive results and discussion on different data dimensionalities that can prove beneficial for prediction on longer time horizons (up to $$2\,\textrm{s}$$ ). We also show results of our approach on two widely employed datasets in AD research, i.e., Cityscapes and BDD100K. We report two types of mIoUs as we have investigated with self generated ground truth labels (mIoU $$^{seg}$$ ) for both of our dataset and actual ground truth labels (mIoU $$^\textrm{gt}$$ ) for a specific split of the Cityscapes dataset. Our method achieves $$57.12\%$$ and $$83.95\%$$ mIoU $$^{seg}$$ , respectively, on the validation split of BDD100K and Cityscapes, for short-term time horizon predictions (up to $$0.2\,\textrm{s}$$ and $$0.06\,\textrm{s}$$ ), outperforming the current state of the art on Cityscapes by $$13.71\%$$ absolute. For long-term predictions (up to $$2\,\textrm{s}$$ and $$0.6\,\textrm{s}$$ ), we achieve $$37.96\%$$ and $$63.65\%$$ mIoU $$^{seg}$$ , respectively, for BDD100K and Cityscapes. Specifically on the validation split of Cityscapes with perfect ground truth annotations, we achieve $$67.55\%$$ and $$63.60\%$$ mIoU $$^\textrm{gt}$$ , outperforming current state of the art by $$1.45\%$$ absolute and $$4.2\%$$ absolute with time horizon predictions up to $$0.06\,\textrm{s}$$ and $$0.18\,\textrm{s}$$ , respectively.
Analisis Penerimaan Siswa terhadap Learning Management System (LMS) E-School dengan Model UTAUT di SMA Negeri 1 Boyolali

E-School is an application and web-based Learning Management System (LMS) designed and developed by SMA Negeri 1 Boyolali. E-School has various features that support academic and non-academic activities which have been used since the beginning of the Covid-19 pandemic. Various new features are added in each version update as an evaluation and improvement of the LMS. However, an evaluation based on students' responses to the LMS E-School to identify students' acceptance and use of E-School has never been done. The purpose of this study is to determine the determinant factors that can affect student acceptance of LMS E-School with the Unified Theory of Acceptance and Use of Technology (UTAUT) model proposed by Venkatesh, et al. (2003). The sample used was 289 out of 1,044 students of SMA Negeri 1 Boyolali for the 2022/2023 academic year, which were selected using a proportionate stratified random sampling technique. This study used a quantitative method with a questionnaire instrument and was analyzed with PLS-SEM assisted by SmartPLS version 3 software. Based on the test results with path analysis and t-statistics, it can be concluded that the variables of performance expectancy and social influence have a positive and significant effect on behavioral intention variables. As well as the variables of facilitating conditions and behavioral intentions have a positive and significant influence on the use behavior variable. Meanwhile, the effort expectancy variable does not have a positive and significant effect on the behavioral intention variable in using the LMS E-School at SMA Negeri 1 Boyolali.
Il dinamismo linguistico dell'italiano tra norma e adattamento ai mezzi di comunicazione

This article aims to describe how the pressure wielded by the means of communication, with their affordancesand techincal restrictions, has been contributing to shape not only the language usage specific to each means,but the overall current common usage of the Italian language. The grammatical norm codified by the grammarbooks, the standard variety, is incomplete with respect to the real usage of the speakers, given that it describesa variety of the language, neglecting the many lexical, syntactic, and textual possibilities existing in the usage.Speakers, in fact, select linguistic forms and constructs on the basis of different, interdependent factors and thestandard usage itself is influenced by innovative forms continually emerging from usage. Language changeover time also intertwines with the synchronic axes of variability, including the one relating to the influence ofthe channel used for communication. When the message passes through a medium, it must undergo anadaptation to its system of rules. The passage always involves a coding, which influences the form of themessage. Linguistic usage, then, is the result of an interweaving of interdependent factors, among which thegrammatical norm and the means of communication are of pivotal importance. Both of these factors imposerules, i.e. restrictions, on usage, shaping and defining it; at the same time, however, usage, by its creative andinnovative nature, constantly pushes the rules, shaping the norm and finding ways to adapt the means ofcommunication to hybrid formats. In this dynamism the language changes, it abandons worn patterns andinvents new ones, adapting itself to the world and adapting the world to itself.
La didattica e la zona grigia della grammatica italiana

We try to shed light on some problems of Italian school grammar, such as the difference between verbalpredicate and nominal predicate, the typology of complements according to traditional logical analysis andvalential grammar, the difference between transitive and intransitive verbs, the typology of parts of speech andother classifications (e.g. the distinction between nouns, adjectives, and pronouns, between conjunctions andadverbs, between coordinates and subordinates, etc.). Starting with the concept of the «grey area of thelinguistic norm» (Serianni 2006), we intend here to broaden the discourse to the grey area of grammar. Herewe mean by grey area all those cases in which the same phenomenon is treated in a very different way bydifferent grammars, or cases in which there are big differences between the grammatical treatment and theinterpretation of common speakers. These fluctuations in the treatment of school textbooks do not depend onthe weakness of the linguistic system, but are often the symptom of inadequate focus on certain themes,especially syntactic ones. Despite the diversity of grammatical traditions, the average Italian school grammaris still too tied to prescriptivism, to hyper-logical interpretations, to a merely nomenclatory perspective in thedescription of linguistic phenomena, rather than to analytical observation and the invitation to metalinguisticreflection. School tradition still seems intent on attributing an ontological value to classifications, when instead,in the light of the most up-to-date linguistic theories, categories once established have been radicallyreconsidered. Finally, proposals for updating grammar teaching will be made, in an attempt to bridge the gapbetween the latter and linguistic reflection, starting from the phenomena listed above.
Unsupervised Learning of Dirichlet Compound Negative Multinomial Mixture Model using Minorization-Maximization Approach

The Dirichlet Compound Negative Multinomial (DCNM) distribution is a probabilistic model used to model multivariate count data. The presence of the Gamma function in the density probability distribution increases significantly the time and computation complexity, especially for high-dimensional datasets. In this paper, we use an alternative parametrization of DCNM using rising polynomials. Then, we use the Minorization-Maximization approach, to learn the parameter estimates of the DCNM mixture model, which brings higher numerical stability, lower tendency of poor local maxima, initialization independence, and avoids cumbersome derivative calculations. Moreover, we propose a new learning method to simultaneously update the mixture’s parameters and find the optimal number of clusters, using the Minimum Description Length Criterion. The proposed approach is validated in two interesting applications, namely, topic detection and scene recognition, where the results demonstrate the robustness and efficiency of our new learning approach.
Destruction-Restoration Suppresses Data Protection Perturbations against Diffusion Models

Diffusion models have become popular in computer vision applications due to their ability to generate high-quality images quickly, with some models achieving a high degree of realism. However, the use of these models for novel image manipulation and generation poses significant risks of being used for harmful purposes, such as the spread of misinformation or copyright infringement. Recent research has proposed techniques that involve adding imperceptible perturbations to images to mitigate privacy and copyright concerns. However, trivial image processing methods can largely destroy these protective perturbations. In this paper, we propose a new method called DiffCleaner, a destruction-restoration pipeline, that combines the advantages of image corruption and restoration methods. It can not only remove protective perturbations but also further preserve high quality generation, as well as fidelity of the original features in generated images. DiffCleaner produces specialized images with visualization and quantitative metrics closely matching unprotected results. We pitch a range of circumventing methods vs. two state-of-the-art data protection methods against diffusion models, and show that both are fragile and offer inadequate data protection. By providing a benchmark comprising a range of protective perturbations removal methods, we hope this paper will inspire and facilitate new research towards developing more resilient methods for data protection.
Attention Enhanced Network with Semantic Inspector for Medical Image Report Generation

Medical report generation can be helpful in diagnoses. Despite the previous efforts of researchers, current models still need improvements in the extraction of image features and quality of generated reports. In this paper, we propose an attention enhanced network with semantic inspector (AENSI) as a new automatic medical report generation model, which serves to help doctors get a high-quality report. For the model, we propose double-weighted multi-head attention as our attention module, where different heads are aggregated with double weights (DWMHA) to enhance its power in catching subtle features and drawing correlations between images and texts. To prevent the drawback of imprecise multi-label classification modules used in current generation models, we design a novel module following decoder that treats tags as inspectors of the generated reports, namely Tag Inspector, as a substitute for the previous classification module. Experimental results of AENSI achieve to the level of state-of-the-art. On IU X-ray, our model surpasses all previous works on every metrics; on PEIR Gross, our model ranks first on BLEU-4 and ROUGE and closely approaches the best on other metrics.
Architecture for Confidential Digital Asset Transfer on Blockchain Through Obfuscation

Blockchains and related applications continue to grow as an area of technology that can provide advantages over traditional approaches in certain use cases. One such area are data markets. Data market use cases include secure asset transactions, data lineage and data governance between multiple parties and involves data security, immutability, transparency. Data markets require strong privacy guarantees for digital asset procurement. The inherent properties of a blockchain can provide significant benefits in the data-market use case. In fact, there are many commercial platforms currently leveraging blockchain technologies for blockchain-based data markets. Leveraging blockchain for data market use cases creates the opportunity for new logical architectures to preserve privacy in the blockchain ecosystem. This publication proposes a multi-faceted solution for stronger privacy guarantees for digital asset procurement on blockchain-based data markets. An architecture will be detailed which provides better guarantees of privacy to those acquiring digital asserts from blockchain-based data markets by leveraging time-delays, batch requests, anonymization, and virtual “one-time” users to form a layer of obfuscation around requests.
Authentic Dialogue Generation to Improve Youth’s Awareness of Cybergrooming for Online Safety

This paper deals with a cybergrooming and sexual misconduct topic in artificial intelligence-based educational programs. Although cybergrooming has been recognized as a cybercrime, there is a lack of programs to protect youth from cybergrooming. We present a generative chatbot framework, SERI (Stop cybERgroomIng), that can generate fluent authentic conversations in the context of cybergrooming between a perpetrator chatbot and a potential victim chatbot. Furthermore, we propose deep-reinforcement-learning-based dialogue generation with a stage-related reward to lead the conversation to the expected stage. We also minimize potential ethical issues introduced by the perverted languages when deploying the chatbots for cybersecurity education programs. We evaluated the conversations of SERI with open-source referenced, unreferenced metrics and human evaluation. We developed SERI as a platform for deploying perpetrator chatbot to interact with youth users to observe their responses and collect reactions when they are asked for private or sensitive information by the perpetrator.
Topological Analysis and Approximate Identification of Leading Lines in Artworks Based on Discrete Morse Theory

Accomplished artists often incorporate leading lines into their compositions to guide the observer’s attention. Although saliency maps are typically employed to locate attractive regions in still images, such scalar features do not express the trajectory of an observer’s gaze. In this study, we propose a method for the visual analysis and approximate identification of leading lines based on maximum graphs, sparse subsets of Morse–Smale complexes, extracted from saliency maps. We provide empirical evidence substantiating the feasibility of our method through a comparison with actual observers’ eye tracking results. Further, we investigate the limitations of our approach by employing it to analyze a variety of artworks with diverse styles.
On Semantics of Hierarchical Planning Domain Models with Decomposition Constraints and Empty Methods

There are multiple formalisms describing hierarchical planning domain models, however many of them do not show semantics of some features such as empty decomposition methods and extensive constraints. In this short paper we describe the semantics of a hierarchical domain model with these extensive constraints and show how empty decomposition methods would work within it. We also compare this model with other models and present some transformations of model properties.