Reviewerly/exHarmony huggingface dataset:
Teacher Agency and Reading Competency Development in Students

The research focuses on the development of reading competency in students from the standpoint of teacher agency. The importance of this article is justified by the function of reading as a key learning and academic achievement skill, as well as the fact that Russian pupils conti­nue to do poorly in text reading and comprehension. The authors present a reading competency&#x0D; framework for general education that consists of five components: 1) reading ability; 2) high reading motivation; 3) ability to work with a book, basic reading skills (information search and analysis; text comprehension and interpretation; assessment of content and form); 4) extensive reading experience and desire to read extracurricular books; 5) willingness and ability to discuss what has been read orally and/or in writing. The researchers identify the teacher’s role in the development of each of the five components in general school children. It is demonstrated that a teacher with advanced agency skills shows a consistent interest in the development of students’ reading competency (motivational component); has relevant knowledge about the content of this competency and the methods of its development, teaches students book-working techniques (cognitive component); and enriches the classroom and school life with exciting reading activities (behavioural component).
Local Adversarial Attacks for Understanding Model Decisions

Recently, deep learning models have demonstrated outstanding performance, but the lack of interpretability makes it difficult to apply them to high-risk tasks. Many existing post-hoc explanation methods have been proposed to explain specific model predictions, but they mostly focus on which locations influence the model decision. In other words, they only provide the location information of discriminative regions but cannot provide the visual differences of the relevant categories in these regions. In this paper, a simple yet effective approach is proposed to explain specific model prediction, which uses visual differences between different categories to provide more intuitive explanations. The basic idea is to use adversarial attacks to transform discriminative features in local regions of an image into features of another class, thereby achieving an explanation of the model's predictions. In this way, the location difference between the original class and the target class in the image will be localized, and an intuitive explanation will be provided through visual changes within the region. Extensive evaluation demonstrates that the proposed method can accurately and adaptively locate discriminative regions in images, and provide users with an intuitive explanation of the classification basis for model predictions through visualization results.
RESEARCH ON KEYWORD MINING OF ACADEMIC LIBRARY SUBJECT SERVICE

This research aims to explore the application of keyword mining technology in academic services of university libraries, and conducts in-depth research on the algorithm and technology of keyword mining technology, the analysis method and application of keyword mining results, the evaluation indicators of keyword mining results, and the design and implementation of optimization strategies. Empirical research has shown that the subject service model based on keyword mining can effectively improve the level of subject service in libraries, providing users with more accurate and comprehensive information resource services. However, there are still some limitations and shortcomings in this study. In the future, we can continue to explore the optimization of keyword mining technology and combine it with other artificial intelligence technologies to develop more intelligent and personalized subject service models. In a word, this study provides some valuable ideas and methods for academic library discipline services, which has certain theoretical and practical significance.
Pengaruh Role Playing Berbantuan Media Audio Visual Terhadap Keterampilan Berbicara Siswa Sekolah Dasar Kelas IV

This research was conducted to compare the application of role-playing models assisted by audio-visual media with conventional models assisted by media images on students' speaking skills. A sample of all grade IV students at SDN 1 Duren, totaling 15 people, was used as the experimental class and at SDN 1 Dermosari, totaling 12 people, was used as the control class. The research method is quantitative with the non-equivalent form of pretest and posttest control group design. The data collection was in the form of speaking skills test with reference to the speaking skills rubric. After analyzing the collected data, the researcher conducted a hypothesis test to find out a significant difference. Hypothesis test analysis data obtained 0.001 &lt;0.05. The researcher concluded that there was a positive impact from the role-playing model assisted by audio-visual media on students' speaking skills which increased and enthusiasm for learning activities. It can be interpreted that the speaking skills of students at SDN 1 Duren are influenced by the role-playing model assisted by audio-visual media. It can be seen from the average results of the experimental class getting an average of 46.93 with a value of 68 as the highest score and 32 as the lowest score while the posttest experiment with an average of 85.86 with 96 as the highest score and 76 as the lowest score.
Analysis of the Best Optimizer Used by Convolutional Neural Network Algorithms in Detecting Masked Faces

Face mask detection is a way to detect whether someone is wearing a face mask or not. The use of face masks is very important in preventing transmission of the Covid-19 virus. Even though the Covid-19 crisis has ended, the use of masks is still recommended for the public when in crowds, especially when in closed rooms. Therefore, in several meeting buildings there are face mask detection devices. These face mask detection tools are often less accurate in detecting masked faces. This inaccuracy can be caused by less than optimal detection algorithms used such as CNN. The Convolutional Neural Networks (CNN) method is a type of deep learning model that is popularly used in facial mask detection tools and various other types of deep learning-based tools. Through the literature study process, previous research found that CNN can use several optimizers to speed up the training process and increase its accuracy. However, it is not known which optimizer has the most optimal capabilities in the CNN algorithm. Therefore, in this research, a comparison of optimizers was carried out in the model training process using a CNN architecture called VGG 16. VGG16 is a deep neural network architecture consisting of 16 layers. The VGG16 architecture has 13 convolution layers, 2 fully connected layers and 1 classifier layer. In this research, there were three optimizers whose quality was tested, namely SGD, RMSProp, and also Adam. The research results show different effects of each optimizer on model performance, where the Adam optimizer provides the highest accuracy of 97.2%, RMSProp provides an accuracy of 96.92%, and SGD provides the lowest accuracy of 66.85%. Then in the data loss variable, Adam gave the lowest loss of 0.0756, RMSProp gave a loss of 0.0883, and SGD with the highest loss of 0.6124. In this research, it was found that the best optimizer used on CNN with the VGG16 architecture was the Adam optimizer with the best accuracy and data loss results among the other 2 optimizers. This can be seen in the in-depth comparative analysis presented in the paper to help select the right optimizer to increase the effectiveness of face mask detection.
Digital University Concept: Structure, Management, and Pedagogical Process Organization

The research focuses on the concept of a digital university, its structure and management, as well as the arrangement of the educational process. According to an examination of academic papers, approaches to the establishment of a digital university may be classified into two broad categories: a digital university as a commercial model and a digital university as a public benefit. The authors propose a notion of a digital university using an integrative approach, merging four interdependent elements: digital format, digital environment, digital resources, and digital platform. A digital format is a type of combination of digital technology (a form of interaction) and content (an act of interaction). The digital environment is a space where interaction takes place. Digital resources are tools for activating the concept of a digital university (used by participants). A digital platform is an algorithm; a method of implementing the concept of a digital university (interaction implementation); and a platform for interaction (a structure where interaction occurs). The authors have created a digital university mana­gement structure by fusing the notion of a digital university with traditional management theory, which includes forecasting, planning, organization, incentive, coordination, and control for formulating and attaining the institution’s aim. As a fundamental strategy of managing the digital transformation of universities, the researchers propose an inclusion policy and an ecosystem approach. The authors have also developed a pedagogical activity model in the context of higher education digitalization, which encompasses educational process organization, teaching, value exchange, extracurricular activities, research activities, self-education, and advanced training. The study also examines future development potential, with the ultimate goal of realizing the university 4.0 concept.
LIBRARY AND INFORMATION SERVICES IN THE DIGITAL AGE: OPPORTUNITIES AND CHALLENGES

In the heart of the information age, academic libraries stand at the forefront of a transformative juncture shaped by technological evolution and changing user needs. This paper comprehensively explores academic libraries’ opportunities and challenges in this dynamic digital era. Historically, libraries have served as repositories of human knowledge and wisdom. Today, they are reshaping themselves, not merely as static centers of information but as active facilitators of knowledge dissemination and creation. From harnessing the potential of DNA-encoded chemical libraries to accelerate drug discovery processes to fervently advocating for open access initiatives, academic libraries showcase their remarkable adaptability and unwavering commitment to pushing the boundaries of traditional information services. However, with every digital stride forward comes a set of intricate challenges. As the paper discusses, libraries grapple with implementing advanced technologies like Linked Data, striking an equilibrium between growing digital resources and invaluable print collections and steering through the maze of leadership complexities in an ever-evolving digital domain. Drawing from a series of illustrative case studies, the paper provides a nuanced understanding of how libraries, despite these challenges, have devised strategic solutions that address immediate concerns and chart a course for future evolution. As the narrative progresses, it sheds light on prescient strategies that libraries can employ to remain relevant and impactful in a future dense with technological advancements. These strategies underscore the pivotal role libraries will continue to play in supporting academic endeavors, fostering digital scholarship, and upholding the ideals of open and inclusive knowledge sharing. The paper concludes with a reflective synthesis, emphasizing the perennial importance of libraries. Regardless of the prevailing technological medium or the nature of challenges ahead, the fundamental ethos of libraries—to enlighten, empower, and engage communities—remains unchanged. This steadfast essence, combined with their resilience and innovation, ensures that libraries will persist as quintessential centers of academic excellence and communal growth.
DESIGN AND RESEARCH OF AUTOMATIC STATION BUFR DATA EMERGENCY BACKUP PLATFORM BASED ON HYBRID PROGRAMMING

Relying on the domestic meteorological transmission and communication system and NAS backup mechanism, based on the relevant standards and specifications of the meteorological big data cloud platform, in the case that the “Tianqing” interface cannot obtain data in a timely and effective manner, detailed requirements, detailed research, and clear implementation steps are made to provide users with reliable meteorological data services. Through the design and research of automatic station BUFR data rain express emergency backup system in this paper, the BUFR data for cell turn decoding, the specific factor value processing, can provide users with continuous data service, for business personnel in flood season normal work to provide basic support. The application shows that the sustainability of the data service is enhanced, and it provides a strong guarantee for the acquisition of rainfall elements in flood season.
Explanation and Knowledge Acquisition in Ad Hoc Teamwork

State of the art frameworks for ad hoc teamwork i.e., for enabling an agent to collaborate with others “on the fly”, pursue a data-driven approach, using a large labeled dataset of prior observations to model the behavior of other agents and to determine the ad hoc agent’s behavior. It is often difficult to pursue such an approach in complex domains due to the lack of sufficient training examples and computational resources. In addition, the learned models lack transparency and it is difficult to revise the existing knowledge in response to previously unseen changes. Our prior architecture enabled an ad hoc agent to perform non-monotonic logical reasoning with commonsense domain knowledge and predictive models of other agents’ behavior that are learned from limited examples. In this paper, we enable the ad hoc agent to acquire previously unknown domain knowledge governing actions and change, and to provide relational descriptions as on-demand explanations of its decisions in response to different types of questions. We evaluate the architecture’s knowledge acquisition and explanation generation abilities in two simulated benchmark domains: Fort Attack and Half Field Offense.
Theoretical Heritage of Leningrad / St. Petersburg Philosophy

The analysis of the theoretical heritage of philosophical science in Leningrad / St. Petersburg appears relevant for a better understanding of the characteristics of how philosophy was formed and shaped in Russia. There were various philosophical schools and trends (philoso­phy of science and culture, social philosophy and history of philosophy) in the city, which had a signi­ficant impact on the national philosophical science. Of crucial value is the concrete analysis of institutional and socio-cultural aspects of the development of philosophical culture in St. Petersburg, as well as the study of the most important results of the philosophers who worked in Leningrad/St. Petersburg, the forms and methods of how they mastered the world’s philosophical thought – thereby significantly expanding the understanding of the theoretical and axiological features of Russian philosophy and culture.&#x0D; The paper primarily focuses on the philosophy of science and culture in Leningrad/St. Petersburg in the latter half of the 20th and early 21st centuries, as these philosophical subjects were the ones that caused the significant transformation of philosophical knowledge from dogmatic Marxism to a multitude of different philosophical products. The authors analyse the ideas of members of the Leningrad school of philosophy of science: Vladimir Svidersky, Vladimir Bransky, Vasily Tugarinov, and Victor Stoff; these figures, through the synthesis of scientific and philosophical concepts relevant in Europe of the 20th century and the original interpretation of Marxism, produced solutions to other philosophical problems, thus initiating the emergence of new philosophical subjects. Also, the authors study how philosophy of culture was developing in St. Petersburg, as represented in the studies of: Moisey Kagan, Yury Solonin, Evgeny Sokolov, Valery Savchuk, Andrey Demichev, Elmar Sokolov, Vyacheslav Sukhachyov; as well as the establishment and development of the history of Russian philosophy through the works of Anatoly Galaktionov, Pyotr Nikandrov, Alexander Zamaleev.
Chinese Continuous Speech Instruction Recognition Method Based on Improved Convolutional Neural Network in Noisy Background

The current structure of Chinese continuous speech instruction recognition in noisy background is generally set to one-way processing. Although it can achieve the recognition goal, the recognition efficiency is low and the error rate is increased. This paper proposes the design, verification and analysis of improved convolutional neural network (CNN) for Chinese continuous speech instruction recognition in noisy backgrounds. The recognition features of Chinese continuous voice commands are extracted by convolutional neural network. The convolutional neural network is improved by adding aggregation layer and improving convolutional layer, and the multi-level voice command recognition structure is set up. Based on this, establish an improved convolutional neural network Chinese continuous voice command recognition model, and use cross enhancement processing to set the recognition range and correct the output by cross-enhancement, achieve voice command recognition. The test results show that: according to the above measurement requirements, 5 Chinese continuous speech segments are selected as the main target objects and divided into 5 corresponding groups. The error rate of the final result is well controlled below 20% in the test stage, indicating that this command recognition method, with the help and support of improved convolutional neural network technology, can further improve the current recognition quality and efficiency.
Comparison of Error Correction and Extraction Approaches

We compare different approaches for error correction detection and error correction. For the error correction detection task, the inputs are the last two utterances of a user and the output is whether there is an error correction in the last utterance. The error correction task gets the same inputs, but the output is the correction of the second last utterance according to the error correction in the last utterance and the extracted pairs of reparandum and repair entity. There are two advantages when using the compared approaches as utility component for a dialog system. It can be avoided to collect corrections for every new domain, and the extraction of the reparandum and repair pairs offers the possibility to learn from them. As benchmark for our comparison, we use an adapted version of the EPIC-KITCHENS-100 dataset. The best approach, a pipeline approach with a fine-tuned sequence labeling BERT model for error correction detection and a fine-tuned sequence-to-sequence T5 model for error correction, has an accuracy of 96.40% on synthetic validation data and an accuracy of 77.81% on human-created real-world test data.
Online-S2T: A Lightweight Distributed Online Reinforcement Learning Training Framework For Resource-Constrained Devices

In deep reinforcement learning (DRL), it is generally resource-constrained devices that interact with real-time dynamic environments, and many DRL models are computationally complex, making online training challenging. Previous research has proposed policy distillation to compress the trained large models into small models thus satisfying the requirements of resource-constrained devices. However, this is usually offline and cannot be suitable for real-time dynamic environments. In this work, we propose an online student-teacher (online-S2T) distributed framework which can deploy multiple smaller student models to resource-constrained devices for online training. Specifically, we decouple the training and interaction of the teacher models and enable the student models to interact with the environment instead of the teacher models. Moreover, to reduce the communication overhead between multiple student models and the teacher model and speed up model convergence, we propose the key experience extraction algorithm. It obtains representative sets of experiences from numerous experience trajectories. The evaluation shows that our approach further accelerates model training and improves the performance of converged models by 25.12% on average, with the ability to deploy smaller models on resource-constrained devices for training.
ECC-based Secure Communication Platform for Unmanned Systems

With the rapid development of information technology, information security has become particularly important in the field of communication. To address the limited resources and low computing power of traditional edge platforms, in this paper, an Elliptic Curve Cryptography-based secure communication platform for unmanned systems was developed. Firstly, the platform adopts the Elliptic Curve Cryptography algorithm as the underlying computing support for secure communication. Besides, in its hardware implementation, multi-level pipeline and three-level parallel computing were adopted to achieve high-speed operation of its critical computing. Secondly, in order to increase the flexibility and programmability of the system, for computing on the secure communication protocol layer, the Linux operating system deployed on ZYNQ-7000 SoC is used to achieve programmability and hardware calls to the secure communication protocol. The results show that the ZYNQ-based secure communication platform can complete a dedicated protocol key negotiation in 12.6ms, which is significantly faster than the 28.8ms computing speed based on the same CPU (dual core ARM Cortex-A9) software implementation. It can improve the speed of secure communication while ensuring the flexibility and programmability of unmanned system applications.
Forget and Regeneration Techniques for Optimizing ASP-Based Stream Reasoning

Answer Set Programming (ASP) is a declarative formalism, developed in the field of nonmonotonic reasoning and recognized as a powerful tool for Knowledge Representation and Reasoning. ASP features show potential also in the Stream Reasoning realm. Nevertheless, such a scenario demands for repeated executions and requires reactive reasoning over rapidly changing data streams. Evaluating ASP programs from scratch at each time point represents a bottleneck. To overcome such limits, incremental reasoning techniques have been proposed. Overgrounding is an incremental grounding technique working under the answer set semantics that fully endorses the ASP declarative nature. Given a non-ground program to be repeatedly evaluated in consecutive time points over possibly differing sets of input facts, overgrounding maintains and enriches an overgrounded program, which eventually converges to a propositional theory general enough to be reused together with possible future inputs. In this work, we focus on developments and extensions of overgrounding that could be beneficial in Stream Reasoning applications. In particular, we present forms of forgetting and regeneration strategies purposely intended to mitigate the typical accumulation-oriented behavior of overgrounding by properly dropping accumulated atoms and/or rules.