Reviewerly/exHarmony huggingface dataset:
MULTIETHNIC EDUCATIONAL ENVIRONMENT AS A FACTOR FOR INTERCULTURAL ADAPTATION OF STUDENTS

The article contains a theoretical analysis of the concept of “multiethnic educational environment of the university” as a significant category in modern pedagogical science. Transformations in the global educational area, rise in the quota for teaching foreign students in Russian universities and increase in demand for Russian educational programs within the framework of education expanse become the main factors which determine the need for a comprehensive study of creating an effective multiethnic educational environment of the university. Such an environment is an important condition for the formation and functioning of the processes of multicultural education, relevant in the context of globalization. The article defines the key approaches to the study of the concept of a multiethnic educational environment, reveals the relationship of this concept with the terms "intercultural educational environment" and "multicultural educational environment". The multiethnic educational environment is considered in the article from several positions: as a condition for successful intercultural adaptation of foreign students in a Russian educational organization; as a set of specially organized conditions for ensuring multiethnic interaction of representatives of different nationalities and faiths in the educational process of higher education, as well as a factor of early prevention of interethnic conflicts in the conditions of current internationalization of higher education. In addition, the article describes the organizational structure of the multiethnic educational environment of the classical university, in which there are six interrelated components forming a system aimed at the formation of intercultural competence of the individual. In the structure of the multiethnic educational environment there are educational, formative, informational, methodological, material and technical components, as well as a component of psychological and pedagogical support of participants in the educational process.
Generative Adversarial Network for Hand-Writing Detection

This paper introduces a Generative Adversarial Network (GAN) for producing images of handwritten numbers from 0–9. This approach generates whole random images of digits in generation phase of GAN. The Detection phase, algorithm tries to detect handwritten image of digits. The GAN algorithm produces variable-sized images by calculating character widths from style vectors. The generator network is trained using GAN, employs a trained handwriting recognition network to make writing understandable. Here we experimented with generator and discriminator oven MNIST dataset and train the model using deep learning to map an image with correct digits.
Steg Crypt (Encryption using steganography)

The target of this venture is to obtain secured encryption and authentication using steganography. So as to accomplish this, numerous organizations &amp; universities in the world have given solutions to secured communication, in the interim many algorithms have been created, including like AES, RSA, LSB etc. But though these algorithms have been developed they were endured to breakdown by hackers which make them obsolete. In this paper we try to combine many already existing algorithms like AES, LSB into one proposed system. Firstly, the utilization of steganography along with traditional encryption is implemented in the proposed system. Second, we try to achieve authentication of user using OTP via E-mail. Thirdly, the encrypted data is divided and sent across many servers so it’s impossible to get complete encrypted data in one path. By applying the proposed model, the probability of data compromise becomes very minimal and very hard to hack.
Towards Efficient Fine-Tuning of Pre-trained Code Models: An Experimental Study and Beyond

Recently, fine-tuning pre-trained code models such as CodeBERT on downstream tasks has achieved great success in many software testing and analysis tasks. While effective and prevalent, fine-tuning the pre-trained parameters incurs a large computational cost. In this paper, we conduct an extensive experimental study to explore what happens to layer-wise pre-trained representations and their encoded code knowledge during fine-tuning. We then propose efficient alternatives to fine-tune the large pre-trained code model based on the above findings. Our experimental study shows that (1) lexical, syntactic and structural properties of source code are encoded in the lower, intermediate, and higher layers, respectively, while the semantic property spans across the entire model. (2) The process of fine-tuning preserves most of the code properties. Specifically, the basic code properties captured by lower and intermediate layers are still preserved during fine-tuning. Furthermore, we find that only the representations of the top two layers change most during fine-tuning for various downstream tasks. (3) Based on the above findings, we propose Telly to efficiently fine-tune pre-trained code models via layer freezing. The extensive experimental results on five various downstream tasks demonstrate that training parameters and the corresponding time cost are greatly reduced, while performances are similar or better.
Catamaran: Low-Overhead Memory Safety Enforcement via Parallel Acceleration

Memory safety issues are the intrinsic diseases of C/C++ programs. Dynamic memory safety enforcement as the dominant approach has an advantage in high effectiveness, yet suffers from prohibitively high runtime overhead. Existing attempts to reduce the overhead are either labor-intensive, tightly dependent on specific hardware/compiler support, or poorly effective.
Beyond “Protected” and “Private”: An Empirical Security Analysis of Custom Function Modifiers in Smart Contracts

A smart contract is a piece of application-layer code running on blockchain ledgers and it provides programmatic logic via transaction-based execution of pre-defined functions. Smart contract functions are by default invokable by any party. To safeguard them, the mainstream smart contract language, i.e., Solidity of the popular Ethereum blockchain, proposed a unique language-level keyword called “modifier,” which allows developers to define custom function access control policies beyond the traditional “protected” and “private” modifiers in classic programming languages.
KeenTune: Automated Tuning Tool for Cloud Application Performance Testing and Optimization

The performance testing and optimization of cloud applications is challenging, because manual tuning of cloud computing stacks is tedious and automated tuning tools are rare used for cloud services. To address this issue, we introduce KeenTune, an automated tuning tool designed to optimize application performance and facilitate performance testing. KeenTune is a lightweight and flexible tool that can be deployed with to-be-tuned applications with negligible impact on their performance. Specifically, KeenTune uses a surrogate model that can be implemented with machine learning models to filter out less relevant parameters for efficient tuning. Our empirical evaluation shows that KeenTune significantly enhances the throughput performance of Nginx web servers, resulting in performance improvements of up to 90.43% and 117.23% in certain cases. This study highlights the benefits of using KeenTune for achieving efficient and effective performance testing of cloud applications. The video and source code for KeenTune are provided as supplementary materials.
ConfFix: Repairing Configuration Compatibility Issues in Android Apps

XML configuration files are widely-used to specify the user interfaces (UI) of Android apps. Configuration compatibility (CC) issues are induced owing to the inconsistent handling of such XML configuration files across different Android framework versions. CC issues can cause software crashes and inconsistent look-and-feels, severely impacting the user experience of Android apps. However, there is no universal solution to resolve CC issues and app developers need to handle CC issues case by case. Existing tools are designed based on predefined rules or visual features that are possibly manifested by CC issues. Unfortunately, they can fail or generate overfitting patches when the CC issues are beyond their capabilities. To fill the above research gaps, we first empirically studied the app developers' common strategies in patching real-world CC issues. Based on the findings, we propose ConfFix, an automatic approach to repair CC issues in Android apps. ConfFix is driven by the knowledge of how an XML element is handled inconsistently in different versions of the Android framework and generates patches to eliminate such inconsistencies. We evaluated ConfFix on a set of 77 reproducible CC issues in 13 open-source Android apps. The results show that ConfFix outperforms baselines in successfully repairing 64 CC issues with a high precision. Encouragingly, the patches for 38 CC issues have been confirmed and merged by app developers.
OCFI: Make Function Entry Identification Hard Again

Function entry identification is a crucial yet challenging task for binary disassemblers that has been the focus of research in the past decades. However, recent researches show that call frame information (CFI) provides accurate and almost complete function entries. With the aid of CFI, disassemblers have significant improvements in function entry detection. CFI is specifically designed for efficient stack unwinding, and every function has corresponding CFI in x64 and aarch64 architectures. Nevertheless, not every function and instruction unwinds the stack at runtime, and this observation has led to the development of techniques such as obfuscation to complicate function detection by disassemblers.
Potential auto-driving threat: Universal rain-removal attack

Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.
A Study on 'Religion in Human Life' According to the 2022 Revised Curriculum

[연구 목적] 2022 개정 교육과정의 종교 교과인「삶과 종교」교육과정 개발 과정과 개정의 주요 사항에 대해 고찰하여「삶과 종교」를 실행할 교사와 종교교육 연구자들이 삶과 종교 교과의 목표와 내용 구성 및 교육과정 실천의 방향에 대한 이해를 도모하는 데 있다. [연구 내용] 2022 개정 교육과정 방향과 설계의 강조점과「삶과 종교」교육과정 개발 과정과 절차에 대해 고찰한다. 그리고「삶과 종교」역점 사항과 주요 내용을 고찰한다. [결론]「삶과 종교」는 진로 선택 교양 교과로서 종교 문해력 함양을 통해 지혜로운 시민을 교육하는 데 목표를 두는 교과이다. 학습자의 삶과 관련한 교육 내용 선정과 조직, 학교 교육과정에서의 실행 가능성 최대화, 교육과정 운영의 주체성과 자율성을 구현할 수 있도록 교과가 설계되었으며 깊이 있는 학습이 가능하도록 교육 내용과 성취 기준이 축소되었다. 그동안 종립학교조차 소극적으로 실행하고, 공립학교에서는 영교육과정으로 남겨진 종교 교과가 개정 교육과정과 고교학점제라는 제도 안에서 더욱 많은 학생들이 선택하고 개설될 수 있도록 제도적 보완이 필요하다. 이는 궁극적으로 교과서 인정 문제와 공립학교에서 종교 교과를 담당하게 될 교사 연수와 연결되는 문제라 할 수 있다.[Objective] To examine the development process and key aspects of the new ‘Religion in Human Life’ curriculum, which is a religious subject in the revised 2022 curriculum, in order to expand the understanding of teachers and religious education researchers. [Contents] 1) The direction and emphasis of the 2022 revised curriculum design and the development process and procedure of the ‘Religion in Human Life’ curriculum. 2) The key focus areas and content of ‘Religion in Human Life’. [Conclusions] ‘Religion in Human Life’ is to educate wise citizens through cultivating religious literacy as a liberal arts subject for career choice. The curriculum was designed to select and organize educational content related to learners’ lives, maximize the feasibility of implementation in the school curriculum, and realize the subjectivity and autonomy of curriculum operation. The educational content and achievement standards were reduced to enable learning in depth. Institutional support is needed so that more students can choose and open religious subjects, which are passively practiced in private schools and left as null courses in public schools, within the system of revised curriculum and high school credit system. This is related to issues such as textbook accreditation and the educating for teachers who will be in charge of religious subjects in public schools.
A Study on the Confucian Curriculum in the Joseon Dynasty from the Perspective of Seonghak (Sage Learning)

이 논문에서는 퇴계 이황(李滉, 1501-1570)과 율곡 이이(李珥, 1536-1584)의 시대를 기준점으로 삼아 크게 그 이전의 조선 전기와 그 이후의 조선 후기로 나누어 성학(聖學)이라는 유학교육(儒學敎育)의 이상에 비추어 조선시대 유학(儒學) 교육과정의 전개 과정을 살펴보았다. 이에 따르면, 조선 전기의 유학 교육과정은 구래의 훈고사장학적 전통이 잔존하는 과도기적 상황에서도 사림파 학자들을 중심으로 점차 성학을 이상으로 하는 성리학 교육과정을 향해 나아갔다. 그리고 퇴계와 율곡의 시대에 이르면, 유학 교육과정은 왕양명식의 심학적 성학이 아니라 정·주 성리학적 성학을 추구하는 명실상부한 성리학 교육과정의 단계에 진입한다. 조선 후기에 들어서면, 조선의 독자 기준에 따라 문묘 향사 제도가 정비되는 등 ‘성학의 자기화(自己化)’가 이루어지는데, 자찬교재(自撰敎材)가 관학과 사학의 유학 교육과정에 정식으로 편입되는 것은 자기화한 성학이 유학 교육과정상에 나타난 좋은 예라 할 수 있다.In this paper, the historical development of the Confucian curriculum of the Joseon Dynasty was examined in light of the ideal of Confucian education called Seonghak (Sage Learning). The review period was largely divided into the early Joseon Dynasty and the late Joseon Dynasty, using the era of Toegye Yi Hwang (1501-1570) and Yulgok Yi I (1536-1584) as reference points. According to this, the Confucian curriculum in the early Joseon Dynasty gradually approached the Neo-Confucian curriculum that idealized Seonghak (Sage Learning) by Sarim scholars even in the transitional situation where the old Hungo-Sajanghak (Confucian exegetics and rhetorics) tradition remained. When the era of Toegye and Yulgok reaches, the Confucian curriculum enters the stage of Seongnihak (Learning of Nature and Principle) curriculum that pursues Seonghak (Sage Learning) based on Cheng and Zhu’s Neo-Confucianism, not Wang yang-ming’s Neo-Confucianism. In the late Joseon Dynasty, self-made textbooks were officially incorporated into the Confucian curriculum of government and private schools, and the sacrificial system of Confucian shrine was reorganized according to Joseon's own standards. Self-made textbooks can be said to be a good example of making Seonghak (Sage Learning) one’s own in the Confucian curriculum.
Investigating the Exchange Patterns of Three Kingdoms Period Burial Vessels in the Yeongsan River Basin through Geochemical Analysis Using pXRF

본 연구는 휴대용 형광 X-선 분광기(pXRF)를 이용한 토기 태토의 지화학적 분석을 통해 영산강유역 분구묘 출토 부장토기의 교환 양상을 검토하였다. 크게 두 가지 문제를 다루었는데, 먼저 분구묘에서 출토된 須惠器(系) 유공광구호를 중심으로 외래계 유물의 유입 혹은 모방 여부를 검토하였다. 분석 결과, 대다수의 영산강 유역 출토품과 태토 성분의 차이를 보여 유입품으로 볼 수 있는 것과 현지 제작 가능성이 높은 것을 파악할 수 있었으며, 기존에 외래계 유물로 논의되지 않았으나 영산강유역 밖에서 제작되었을 것으로 보이는 유물을 추가적으로 확인하였다. 다음으로는 한 유적 출토 부장토기의 제작지 다양성을 살펴보았다. 화학적 성분으로 보았을 때 분묘에서 출토된 토기가 서로 다른 유적 간에 군집이 분명하게 나뉘지 않고 한 유적 출토품이라 하더라도 원소 성분 함량의 편차가 크다는 것이 확인되었다. 대조군인 취락 출토 토기의 분석에서는 유적 간에 군집이 분명하게 나뉘는 것을 보았을 때 분묘 출토품의 분석 결과는 생활토기와는 달리 여러 곳에서 제작된 토기가 한 유적에서 소비되었음을 보여 주며, 영산강유역 내에서 유적 간에 토기 교환이 활발히 이루어진 결과로 이해될 수 있다. 이렇게 교환된 토기는 일종의 조문품으로서 수장계층의 장례에 주변의 여러 집단이 참여했던 상황이 물질자료에 반영된 것이라 할 수 있다.This study examines the exchange patterns of burial vessels excavated from bungumyo tombs in the Yeongsan River basin which were identified through analysis using a portable X-ray fluorescence spectrometer (pXRF). Two main issues are addressed. Firstly, wares thought to have been imported, namely, sueki, were analyzed to determine whether they were imported or locally imitated. Most of the sueki analyzed in this study differed in chemical composition from locally produced ceramics, indicating that they were likely imported, while one sample may have been produced locally. Additionally, some ceramics that are not traditionally classified as imported wares were found to have been produced outside of the Yeongsan River basin. Secondly, exchange patterns of burial vessels within the Yeongsan River basin were examined through comparisons of chemical compositions for artifacts from different archaeological sites. Results showed that even within a single site, variation in chemical composition of burial vessels was significant and did not cluster according to its location of burial. This contrasts with the clear clustering observed in the analysis of utilitarian pottery excavated from settlement sites. This may indicate that pottery produced at multiple locations was placed within a single burial site and pottery used as burial vessels were frequently exchanged between sites in the Yeongsan River basin. It is believed that these items were used as funeral offerings brought by a variety of groups that participated in funerary rituals.
Evolution Axioms

Providing a modern approach to classical fluid mechanics, this textbook presents an accessible and rigorous introduction to the field, with a strong emphasis on both mathematical exposition and physical problems. It includes a consistent treatment of a broad range of fluid mechanics topics, including governing equations, vorticity, potential flow, compressible flow, viscous flow, instability, and turbulence. It has enhanced coverage of geometry, coordinate transformations, kinematics, thermodynamics, heat transfer, and nonlinear dynamics. To round out student understanding, a robust emphasis on theoretical fundamentals and underlying mathematical details is provided, enabling students to gain confidence and develop a solid framework for further study. Included also are 180 end-of-chapter problems, with full solutions and sample course syllabi available for instructors. With sufficient coverage for a one- or two-semester sequence, this textbook provides an ideal flexible teaching pathway for graduate students in aerospace, mechanical, chemical, and civil engineering, and applied mathematics.
Comparison of Virtual Machines and Bare Metal for CNFs at Resource-Constrained Network Edge

A growing number of network functions built to run on commodity compute infrastructure, are increasingly adopting cloud native principles, and therefore often run inside containers. Containerised Network Functions (CNFs) can be hosted directly on the server’s host OS- Bare Metal (BM)- or can run inside a Virtual Machine (VM). The choice of BM or VM to host containers depends on many factors linked to security, tenant isolation, available resources and performance. This paper explores this important consideration by conducting comparative tests in a specific domain, namely at the network edge where resources are tightly constrained. Using a small form-factor micro server, we demonstrate how BM can clearly out-perform the use of a VM but that this occurs when there are distinct set-up dependencies around small fixed frame sizes and very stringent packet loss constraints. The performance margin is notably shrunk however, when a more realistic traffic load is used and non-zero packet loss permitted: the VM performance reaches within 89% of the BM throughput, while also achieving an average latency 14% lower than the BM setup. These results emphasise it is not simply a clear-cut case of BM always being “better”, with many other factors requiring attention including security and tenant isolation. We use the insights gleaned from lab testing alongside qualitative criteria to better inform design decisions around using VMs or BM to host CNFs.
The Possibility of Applying the Theory of Multioperations to Build Self-learning Artificial Intelligence Systems with the Explainability Property

When developing artificial intelligence systems, one of the important aspects is a more accurate description of the considered events and their uncertainties. At the moment, most of the modern solution's mathematical apparatus have a significant drawback – the data is considered only between two logical elements – true and false. In this regard, it is impossible to give detailed description of the considered events and their uncertainties. Another important aspect of Artificial Intelligence systems is the explainability property, since making key decisions, it is necessary to explain of how the AI system came to its answer. One of the possible solutions is to apply the theory of multioperations. The paper suggests a methodology for building self-learning systems of explainable artificial intelligence based on the theory of multioperations and presents an abstract example of the application of the developed methodology for building an AI system of a medical field of study.
Automatic text summarization based on extractive-abstractive method

The choice of this study has a significant impact on daily life. In various fields such as journalism, academia, business, and more, large amounts of text need to be processed quickly and efficiently. Text summarization is a technique used to generate a precise and shortened summary of spacious texts. The generated summary sustains overall meaning without losing any information and focuses on those parts that contain useful information. The goal is to develop a model that converts lengthy articles into concise versions. The task to be solved is to select an effective procedure to develop the model. Although the present text summarization models give us good results in many recognized datasets such as cnn/daily- mail, newsroom, etc. All the problems can not be resolved by these models. In this paper, a new text summarization method has been proposed: combining the Extractive and Abstractive Text Summarization technique. In the extractive-based method, the model generates a summary using Sentence Ranking Algorithm and passes this generated summary through an abstractive method. When using the sentence ranking algorithm, after rearranging the sentences, the relationship between one sentence and another sentence is destroyed. To overcome this situation, Pronoun to Noun conversion has been proposed with the new system. After generating the extractive summary, the generated summary is passed through the abstractive method. The proposed abstractive model consists of three pre-trained models: google/pegusus-xsum, face-book/bart-large-cnn model, and Yale-LILY/brio-cnndm-uncased, which generates a final summary depending on the maximum final score. The following results were obtained: experimental results on CNN/daily-mail dataset show that the proposed model obtained scores of ROUGE-1, ROUGE-2 and ROUGE-L are respectively 42.67 %, 19.35 %, and 39.57 %. Then, the result has been compared with three state-of-the-art methods: JEANS, DEATS and PGAN-ATSMT. The results outperform state-of-the-art models. Experimental results also show that the proposed model is qualitatively readable and can generate abstract summaries. Conclusion: In terms of ROUGE score, the model outperforms some art-of-the-state models for ROUGE-1 and ROUGE-L, but doesn’t achieve good result in ROUGE-2.