Reviewerly/exHarmony huggingface dataset:
Research on Great Wall section protection and user VR experience innovation based on GIS data visualization

VR technology can enable users to be in a realistic virtual environment, thus bringing them an immersive experience. It has great charm and can effectively display scenes that are difficult to reproduce in real life. It can also be used in education and other fields. It has a wide range of applications and can bring great convenience. Under this background, this paper introduces GIS data visualization technology to complete the expansion of VR technology and apply it to the Great Wall section protection research project. This paper has improved a kind of VR system, which can meet the needs of human–computer interaction and create virtual avatars to achieve two-way communication between human and VR experience environment. This system design has the advantages of simple use and powerful functions and is also the design goal of VR experience technology itself. The ease of use of the system is reflected in the visual clarity of interface interaction and system response speed. The practicality is reflected in the stability of system operation and the time required for users to complete feature functions. The security is reflected in the degree of protection of private data. The simulation results show that the system can effectively reduce the magnitude of data after processing and compressing the data set, thus reducing the processing time. Compared with the original processing method, it can be seen that the system improves significantly. By traversing the original dataset, the array can be effectively drawn, which greatly reduces the system processing time and improves the efficiency. In this paper, GIS data visualization technology and VR technology are integrated to complete the research of the Great Wall section protection project.
Association Rules

Healthcare decisions supportable with association rule models include identification of comorbidity patterns in patients, monitoring diseases by geographical area and age group to assess doctor or nurse shortages, and to identify abnormalities detected by real-time sensor data.
Data Mining Methods and Applications

In this chapter, we provide a review of the knowledge discovery process, including data handling, data mining methods and software, and current research activities. The introduction defines and provides a general background to data mining knowledge discovery in databases, following by an outline of the entire process in the second part. The third part presents data handling issues, including databases and preparation of the data for analysis. The fourth part, as the core of the chapter, describes popular data mining methods, separated as supervised versus unsupervised learning. Supervised learning methods are described in the context of both regression and classification, beginning with the simplest case of linear models, then presenting more complex modeling with trees, neural networks, and support vector machines, and concluding with some methods only for classification. Unsupervised learning methods are described under two categories: association rules and clustering. The fifth part presents past and current research projects, involving both industrial and business applications. Finally, the last part provides a brief discussion on remaining problems and future trends.
The Conference “Young Leaders in Library Science” is a Platform for Ideas and Discussions

The International Scientific and Practical Conference “Young Leaders in Library Science” was held for the first time by the Russian State Library (RSL) in September 2022 as part of celebration of the 160th anniversary of the Moscow Public Museum and Rumyantsev Museum with the participation of the National Library of Russia (NLR) and the Section 31 on scientific research work of the Russian Library Association (RLA). The idea of the conference — to identify the present and future leaders of scientific projects and research in the modern library and information sphere of the country — has no analogues. The conference was attended by representatives of the Lomonosov Moscow State University, the National Research University — Higher School of Economics and the leading universities of Moscow that train staff for libraries — the Moscow State Linguistic University and the Moscow State Institute of Culture. For the first time, the results of the All-Russian monitoring of the staffing needs of libraries in 2022 in terms of the presence of young and qualified specialists in the sector were presented to the wide audience. For two days, three sections worked at the conference site: “Scientific research: in libraries, about libraries, for libraries”, “Applied scientific research in the library field: modern approaches and results” and “Library personnel: higher and additional professional education”. The latter was timed to the 100th anniversary of organization of specialized professional training of librarians within the RSL (1922—2022). 511 participants from 9 neighbouring countries have previously registered to participate in the conference. 68 reports and papers of specialists from federal, regional, municipal, university libraries, universities, scientific organizations from 24 regions of Russia and three CIS countries were presented. The speakers were students and postgraduates, 8 doctors of sciences and 29 candidates of sciences, as well as managers and practitioners engaged in applied research as part of their direct work. Following the results of the conference, the collection of the best reports will be published in 2023. 243 full-time participants attended the conference; the broadcasts of the events were viewed 2,243 times only at the time of its closure. The result of the conference was to identify the most relevant topics of scientific research in the area of library science, bibliography science and book studies, as well as specialists who will develop library science in the coming years and who will work in these scientific areas.
Diagnostics of Professional Identity Statuses in Ukraine: Comparison of the Results of Two Questionnaires

The article deals with the problem of using questionnaires to study professional identity in Ukraine. Now there is a significant lack of methods for studying professional identity, the obsolescence of their methodology, as well as the lack of Ukrainian-language tools. The purpose of this study was to compare the results of two methods of studying professional identity statuses in the Ukrainian scientific space – the method of studying professional identity of L.B. Schneider and the questionnaire of professional identity statuses of students by A.A. Ozerina. These two methods are among the most commonly used for diagnosing the professional identity of adult subjects, regardless of their professional affiliation. During the investigation, a cluster analysis was performed to take into account all possible variants of the formation of professional identity statuses according to A.O. Ozerina, after which the results of both methods were compared by calculating percentages, as well as applying the Pearson's chi-squared test. The results showed a weak correspondence between the statuses of professional identity according to L.B. Schneider and A.O. Ozerina: of all the options for calculating statuses and ways to avoid the problem of pseudo-positive identity, only the achieved identity coincided between the two methods in 4.3% -17% of cases from the total number of subjects studied. Especially problematic was the determination of premature identity, the percentage of coincidence between methods for which in none of the calculation options exceeded 2.2% of cases from the total number of subjects studied. In addition, the absence of a statistically significant relationship between the questionnaires considered was determined using Pearson's chi-squared test. Suggestions are made about the inexpediency of using or adapting these questionnaires in Ukrainian.
Load balanced locality-aware parallel SGD on multicore architectures for latent factor based collaborative filtering

We investigate the parallelization of Stochastic Gradient Descent (SGD) for matrix completion on multicore architectures. We provide an experimental analysis of current SGD algorithms to find out their bottlenecks and limitations. Grid-based methods suffer from load imbalance among 2D blocks of the rating matrix, especially when datasets are skewed and sparse. Asynchronous methods, on the other hand, can face cache issues due to their memory access pattern. We propose bin-packing-based block balancing methods that are alternative to the recently proposed BaPa method. We then introduce Locality Aware SGD (LASGD), a grid-based asynchronous parallel SGD algorithm that efficiently utilizes cache by changing nonzero update sequence without affecting factor update order and carefully arranging latent factor matrices in the memory. Combined with our proposed load balancing methods, our experiments show that LASGD performs significantly better than alternative approaches in parallel shared-memory systems.
Publishing Trends in Russia, the Top Projects and Authors of 2022

The author analyses participation of the Russian State Library (RSL) in the International Fair of Intellectual Literature non/fictio№ 24, which took place on December 1—5, 2022. The paper presents the review of new scientific publications of the RSL “Pashkov Dom” Publishing House. The top list of the non/fictio№ 24 includes two new publications of the RSL “Pashkov Dom”: the book devoted to L.M. Koval “The Keeper of the History of Leninka” (2021) and “The Art of Valery Pokatov: Pages of Life and Creative Work” (2021). In 2022, the Russian State Library celebrated the 160th anniversary of the opening of the Moscow Public Museum and the Rumyantsev Museum. The anniversary stand of the RSL presented publications on the history of the library, catalogues of the collections and exhibitions, issues of the journals “Observatory of Culture”, “Bibliotekovedenie” (Library Science), etc. The leaders of sales were the collection of articles “‘This very first seal was beautiful: round, dimensional, pureʼ. The book culture of the era of Peter the Great” (2022), the publication of the comedy by A.S. Griboyedov “Woe from Wit” (2022), the album-catalogue by S.P. Utva “Gifts of the Imperial House to the Moscow Public Museum and Rumyantsev Museum” (2022). Of particular note is the monograph “Essays on the Development of Bibliographic Activity of the Russian State Library” (2022) and the second edition of the collective work “Scientific Research in Libraries: Subject Themes, Organization, Presentation of Results” (2023). The article summarizes the publishing trends in Russia formulated by the experts on December 2, 2022 at the discussion “Digitalization of book production and the future of the industry, skills of forming a comprehensive publishing project” in the framework of non/fictio№ 24. The significant event of 2022 was the 17th season of the “Big Book” National Literary Award, established by the Centre for the Support of Russian Literature. The solemn ceremony of awarding the prize winners took place on December 8, 2022 at the Pashkov House of the Russian State Library.
Online intervention siamese tracking

Online target update excels in helping a visual tracking algorithm adapt to variations in target appearance during inference, and thus is preferred by recent advances. However, this technique typically operates in a heuristic manner by updating numerous network parameters with large amounts of online-collected data. This poses a distinct challenge: can the online target update be effectively executed without using the aforementioned approach? To this end, we propose a novel target feature update scheme to reduce the need for tedious data collection and computation-intensive parameter updates. This scheme operates on the principle of causal intervention and is just as effective as default parameter updates in visual tracking. Besides, we explore a novel video-specific target label to capture the context of a specific target in video frames for feature discrimination. This makes target features better fit for appearance changes. Such schemes together with the off-shelf pre-trained classification backbone form a novel online intervention siamese tracker (OIS). When equipped with an unsupervised pre-trained backbone, OIS outperforms current state-of-the-art unsupervised trackers on the OTB and VOT. When exploiting a supervised trained backbone, it competes with typical supervised trackers trained on massive offline training and online tracking data.
GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems

Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, which, unfortunately, cannot handle high-dimensional systems and hence cannot be applied to most real-world dynamical systems. This work proposes GoSafeOpt as the first algorithm that can safely discover globally optimal policies for high-dimensional systems while giving safety and optimality guarantees. We demonstrate the superiority of GoSafeOpt over competing model-free safe learning methods on a robot arm that would be prohibitive for GoSafe.
Identification of image global processing operator chain based on feature decoupling

Image authenticity verification is an important issue to be studied, which has attracted growing attention recently. Most of the existing forensic methods are aimed at detecting a specific manipulation. However, due to the superimposed processing artifacts caused by using different operations to forge images, the image global processing operator chain identification, which is composed of multiple global manipulations in a certain order, remains a challenge. In this paper, we focus on detecting multiple manipulations and identifying the order of these manipulations. By analyzing the relationship between blind signal separation and operator chain identification, we find that the independent source features of different operations will be coupled when the image is processed by multiple operations, which is similar to what in blind signal separation. Therefore, it is reasonable to formulate the problem of operator chain identification with blind signal separation. Then, a feature decoupling method is proposed to estimate the source feature from the coupled features by optimizing a decoupling matrix. These estimated decoupled features are valid evidence for operator chain identification. For the realistic scenario where images are saved in JPEG format, the comparison with some state-of-the-art methods demonstrates that the proposed method could identify operator chains with better performance.
Few-shot learning CNN optimized using combined 2D-DWT injection and evolutionary optimization algorithms for human face recognition

Convolutional Neural Network (CNN) has shown remarkable success in the area of machine vision. The purpose of this research is to enhance the classification for the few-shot learning datasets by developing a robust feature extraction system using an optimized CNN model. The aforementioned goal is attained in the following way by developing two classification models, (1) CNN optimized using Two-Dimensional Discrete Wavelet Transform (2D-DWT) injection using Principal Component Analysis (PCA), and Grey Wolf Optimizer (GWO), and (2) CNN optimized using 2D-DWT injection using PCA and Multi-Verse Optimizer (MVO) algorithm. This optimization process enhances the rate of face recognition for the small training dataset by extracting maximum features. Experiments on the AT&amp;T (ORL), LFW, and Extended Yale-FACE-B databases show that the technique improves results significantly, with recognition rates increasing to 100% for training accuracy on all datasets and 100%, 98%, 97% on ORL, LFW, and Extended Yale-FACE-B datasets, respectively, for testing accuracy.
Towards an Empirical Study to Determine the Effectiveness of Support Systems against E-Mail Phishing Attacks

E-mail phishing attacks are still the number one gateway for attackers. Even when the patch level of a network is up to date, if one employee clicks on a link in a phishing e-mail and enters their credentials on a malicious website or downloads malware, the whole organization might get compromised. Anti-phishing support systems highlight different aspects of an e-mail to help users to detect phishing e-mails. However, little is known about their effectiveness, especially in comparison to each other. This paper presents our experimental design to investigate the efficacy of various support systems. For this purpose, we created a fictional scenario and an interactive tool to display e-mails. In addition, we present our preliminary study with the first results to classify test e-mails in different difficulty levels that serve as a basis for our main study.
Three-Dimensional Human Pose Estimation with Spatial–Temporal Interaction Enhancement Transformer

Three-dimensional human pose estimation is a hot research topic in the field of computer vision. In recent years, significant progress has been made in estimating 3D human pose from monocular video, but there is still much room for improvement in this task owing to the issues of self-occlusion and depth ambiguity. Some previous work has addressed the above problems by investigating spatio-temporal relationships and has made great progress. Based on this, we further explored the spatio-temporal relationship and propose a new method, called STFormer. Our whole framework consists of two main stages: (1) extract features independently from the temporal and spatial domains; (2) modeling the communication of information across domains. The temporal dependencies were injected into the spatial domain to dynamically modify the spatial structure relationships between joints. Then, the results were used to refine the temporal features. After the preceding steps, both spatial and temporal features were strengthened, and the estimated final pose will be more precise. We conducted substantial experiments on a well-known dataset (Human3.6), and the results indicated that STFormer outperformed recent methods with an input of nine frames. Compared to PoseFormer, the performance of our method reduced the MPJPE by 2.1%. Furthermore, we performed numerous ablation studies to analyze and prove the validity of the various constituent modules of STFormer.
Mixed-Initiative Interaction with Computational Generative Systems

Machine learning models provide functions to transform and generate image and text data. This promises powerful applications but it remains unclear how users can interact with these models. With my research, I focus on designing, implementing, and evaluating functional prototypes for understanding human-AI interactions. Methodologically, I focus on web-based experiments with a mixed-methods approach. Furthermore, I use these prototypes and generative models as a material to understand fundamental concepts in human-AI interactions, such as initiative, intent, and control. In an already conducted study, for example, I showed that the levels of initiative and control afforded by the UI influence perceived authorship when writing text. For the future, I plan to carry out more studies on collaborative writing. With my dissertation, I contribute to how we will build human-AI interactions and how we will collaborate with computational generative systems in future.
Futuring from an indigenous community stance: projecting temporal duality from the past into the future

This paper presents the first instance and experience of futuring with a rural San community from the Kalahari desert in Donkerbos, Namibia. Over a series of sessions we explore divergent speculative design and design fiction methods to stimulate and invoke alternative green energy use cases. These alternatives are premised on the imagination of unorthodox green energy use, superseding interventionist energy use which is constantly propagated and mainstream. We showcase the application of speculative design and design fiction in challenging the dominant interventionist approach and singular temporal view, resulting in a dissentient dual temporality. As well as demonstrate its utility and inadequacies in transitioning an African rural indigenous community into the speculative, arguing for the appropriation and widening of futuring methods in an African context.
A general construction of regular complete permutation polynomials

Let $$r\ge 3$$ be a positive integer and $${\mathbb {F}}_q$$ the finite field with q elements. In this paper, we consider the r-regular complete permutation property of maps with the form $$f=\tau \circ \sigma _M\circ \tau ^{-1}$$ where $$\tau $$ is a PP over an extension field $${\mathbb {F}}_{q^d}$$ and $$\sigma _M$$ is an invertible linear map over $${\mathbb {F}}_{q^d}$$ . When $$\tau $$ is additive, we give a general construction of r-regular CPPs for any positive integer r. When $$\tau $$ is not additive, we give many examples of regular CPPs over the extension fields for $$r=3,4,5,6,7$$ and for arbitrary odd positive integer r. These examples are the generalization of the first class of r-regular CPPs constructed by Xu et al. (Des Codes Cryptogr 90:545–575, 2022).
Digital Literacy: Perceptions of Primary School Teacher Education Students

This research is motivated by the emergence of the Covid-19 pandemic, which resulted in a change in the education system from conventional to online. This condition requires students to adapt to changes in the learning system. This study aims to analyze the perceptions of PGSD students regarding digital literacy in the era of the Covid-19 pandemic. This type of research is quantitative. This research uses a quantitative descriptive approach with a survey method. The population in this study were all 2018 class students, with a sample of 76 students. The data collection method uses a questionnaire with the help of Google Forms. The instrument used to collect data is a questionnaire. The data analysis technique uses descriptive analysis with the help of SPSS and Microsoft Excel. The results of the study show that, in general, the level of perception of PGSD students regarding digital literacy in the era of the Covid-19 pandemic is in the "Medium" category. It implies that the campus must pay more attention to the needs of its students. What can be done is to help increase digital literacy, especially by utilizing online platforms and operating learning applications.
Privacy-Preserving Fully Online Matching with Deadlines

In classical secure multi-party computation (SMPC) it is assumed that a fixed and a priori known set of parties wants to securely evaluate a function of their private inputs. This assumption implies that online problems, in which the set of parties that arrive and leave over time are not a priori known, are not covered by the classical setting. Therefore, the notion of online SMPC has been introduced, and a general feasibility result has been proven that shows that any online algorithm can be implemented as a distributed protocol that is secure in this setting [22, 23]. However, so far, no online SMPC protocol that implements a concrete online algorithm has been proposed and evaluated such that the practicality of the constructive proof is an open question.