Reviewerly/exHarmony huggingface dataset:
Solving HNP with One Bit Leakage: An Asymmetric Lattice Sieving Algorithm

The Hidden Number Problem (HNP) was introduced by Boneh and Venkastesan to analyze the bit-security of the Diffie-Hellman key exchange scheme. It is often used to mount a side-channel attack on (EC)DSA. The hardness of HNP is mainly determined by the number of nonce leakage bits and the size of the modulus. With the development of lattice reduction algorithms and lattice sieving, the range of practically vulnerable parameters are extended further. However, 1-bit leakage is still believed to be challenging for lattice attacks. In this paper, we proposed an asymmetric lattice sieving algorithm that can solve HNP with 1-bit leakage. The algorithm is composed of a BKZ pre-processing and a sieving step. The novel part of our lattice sieving algorithm is that the lattice used in these two steps have different dimensions. In particular, in the BKZ step we use more samples to derive a better lattice basis, while we just use truncated lattice basis for the lattice sieving step. To verify our algorithm, we use it to solve HNP with 1-bit leakage and 116-bit modulus.
Blockchain analysis of the Bitcoin market. (Part 3)

The detailed analysis of the Bitcoin network and its main participants. The expert authors (Igor Makarov, London School of Economics, Antoinette Schoar, MIT Sloan School of Management) completed the study authorized by the National Bureau of Economic Research (NBER), the US-based private agency. The Bitcoin network is defined as a new database comprising many of public and proprietary sources to link bitcoin address to real object, and an extensive set of algorithms to extract information on market key players behavior. Three major pieces of analysis of the Bitcoin eco-system were conducted. First, the authors analyze the transaction volume and network structure of the main participants on the blockchain. Second, they document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, they analyze the ownership concentration of the largest holders of Bitcoin. The researchers found that 1/3 of all bitcoins issued were owned by 10,000 individual investors. They conclude that the high concentration makes the first cryptocurrency market vulnerable to hypothetical hacker attack. The translator notes that paraphrasing English text in Russian was rather challenging due to the newness of the financial agenda and introduction of the term entity extensively used in the Western countries though new to Russia. Nevertheless, it is necessary to introduce readers to the bitcoin technology which will be also practical and useful for the library and information community.
Modern research practices of professional competence formation

The article analyzes the modern researchers theoretical ideas regarding the concept of professional competencies. The problem of professional competencies formation and development is essential under the current circumstances, as competition among production organizations increases every day. Moreover, employers are interested in highly qualified workers who are ready to achieve effective production results and contribute to the prosperity of companies via the implementation of theoretical knowledge and practical skills. The purpose of the article is to analyze the process of formation of professional competencies among employees. In this article, special attention is paid to the analysis of the most important competencies and skills of employees necessary for the full functioning of labor relations in the production process. Also, in this article, the competence-based approach to the implementation of practice-oriented training at the university for the preparation of potential employees, who are able to compete and adapt to the emergence of the latest modern technologies, has been examined and analyzed. The introduction of a competence-based approach into the higher education system is a new step in the training of highly qualified personnel who has not only a set of theoretical knowledge, but is also ready to implement the practical skills to solve various production tasks. An employee, who has received a high-quality higher education, a rich theoretical set of knowledge and practical skills, will apply his / her abilities in his work, demonstrating high efficiency. The results are presented via practical recommendations for employers and educational organizations concerning the employees formation of professional competencies.
PENGARUH KECERDASAN EMOSIONAL TERHADAP KEMAMPUAN PEMAHAMAN KONSEP MATEMATIKA KELAS VII MTS FISABILILLAH

The purpose of this study was to determine how much influence emotional intelligence had on the ability to understand mathematical concepts of grade VII A and VII B students at MTs Fisabilillah School. The instrument in this study used an essay test of five questions and an emotional intelligence questionnaire of 40 statements. Based on the data that has been obtained from the calculation of the effect of emotional intelligence on the ability to understand mathematical concepts of MTs Fisabilillah School students, the researchers can conclude several things including, there is a significant influence on the influence of emotional intelligence on the ability to understand mathematical concepts of MTs Fisabilillah School students of 0.8359 or 69,87%. The rest is influenced by other factors, such as students' thinking ability, and other aspects not examined by this study. This is also reinforced by the results of hypothesis testing which shows that thitung &gt; ttabel is thitung 11.1778 &gt; ttabel= 2.00488 which states that there is a significant influence between emotional intelligence and the ability to understand mathematical concepts.
Unsupervised Cross-Domain Person Re-Identification Method Combining Improved Squeeze and Excitation Block

The performance of the existing unsupervised cross-domain pedestrian re-identification methods is degraded by the domain gap and unalignment problems. To solve these problems, this paper proposes an unsupervised cross-domain person re-identification method combining improved squeeze and excitation blocks. First, in feature extraction using the ResNet-50 network, improved squeeze and excitation blocks are used among the backbone network to mine fine-grained information. Second, to improve the quality of clustering clusters, an awareness instance selection method is then used to suppress noise interference and remove unreliable instances, and the awareness instance selection method selects images with reliable labels for model training. The experimental results show that the new method designed in the paper improves the accuracy of mAP by 9.4% on the target domain Market-1501 dataset and 6.9% on the target domain DukeMTMC-ReID dataset compared with other typical methods.
Pressure Compensation Method of Check Valve in Aircraft Hydraulic System Based on Artificial Intelligence

In view of the poor application effect of the traditional valve pressure compensation method, this study designed an artificial intelligence-based pressure compensation method for one-way valve of aircraft hydraulic system. Based on the functional block diagram of aircraft hydraulic system, the pressure conflict of aircraft hydraulic system is judged and the feasibility of pressure compensation is analyzed. Based on this, in order to analyze the fault conditions, the unified modeling of multiple systems, including hydraulic energy system, integrated management control system and hydraulic user system, was completed based on Modelica. Then the supplementary design was completed by designing the pressure compensation model of aircraft one-way valve. The experimental results show that compared with the traditional method, the pressure compensation effect of the proposed method is better, and it has higher practical application value.
Determinants of the development of secondary vocational education in the Republic of Crimea

The purpose of the study is to determine the state of the main determinants that contribute to the development of secondary vocational education in the Republic of Crimea. The article describes the conditions for the functioning of institutions of secondary vocational education in the context of key components that ensure its development; considered the personnel crisis experienced by secondary vocational education, the main reason for which was the low level of wages, and as a result, the decrease in the level of prestige of work in institutions of secondary vocational education. As a result of a retrospective analysis of the state of the main determinants of the development of secondary vocational education, proposals were formulated for the development of secondary vocational education, recommended (1) the use of an effective system of motivation and incentives for personnel for quantitative and qualitative staffing of educational activities, (2) updating the material and technical base corresponding to that, which is used at enterprises, (3) the implementation of state support in matters of interaction between vocational schools and real sectors of the economy, (4) constant monitoring of the state of secondary vocational education according to the determinants indicated in the article.
PCT: A Relay-Based Privacy-Preserving Cross-Chain Transaction Scheme

Blockchain has been rapidly evolving in recent years and widely used in financial transactions due to its tamper-proof and decentralization features. However, as a relatively independent system, different blockchains have difficulty interoperating with each other, and the issue of “Value Island” becomes more problematic. In addition, the users on the different ledgers are also concerned about the leakage of privacy in the cross-chain process. In this paper, we propose a Privacy-Preserving Cross-Chain Transaction (PCT) scheme combining the relay mechanism with the hash locking mechanism. Meanwhile, the Pedersen commitment is introduced to enable the committers to verify the transaction without acknowledging the amount. We also conduct the range proof based on the Borromean ring signature to ensure that the transaction amount is legitimate. Finally, a lending scheme is designed to solve the problem of insufficient funds in cross-chain transactions. Security analysis and experiments show our scheme achieves privacy-preserving cross-chain transactions with low overhead.
The Influence of Sentiment on Bank Mandiri (BMRI) Stock Movements Using Feature Expansion with Word2vec and Support Vector Machine Classification

On July 20, 2019, Bank Mandiri experienced a system failure that resulted in the balance of Bank Mandiri customers changing automatically. Some customers experienced a decrease in their balance, and some experienced an increase in their balance. Bank Mandiri's stocks were affected because they encountered a system failure. Customers expressed their opinions about the issue on social media and news media which caused a lot of negative sentiment for BMRI stocks. Due to many negative sentiments, the author will conduct a sentiment analysis process on the effect of BMRI stock price movements to find out how much influence social media and news media sentiment has on BMRI stock prices. This study uses the Support Vector Machine and the expansion of Word2vec to find similar words to have the same word vector value and overcome the word mismatch problem in the case study of Bank Mandiri's system failure.
Retinex-Based Low-Light Hyperspectral Restoration Using Camera Response Model

Spectral quality is one of the most critical issues that has to be considered in real hyperspectral image (HSI) application. Denoising, destriping, inpainting, deblurring and super-resolution are common techniques to improve the quality of HSIs from different aspects. These techniques have attracted much attention that a diversity of methods, algorithms, tools have been well developed to facilitate the development of HSI restoration. Although effectively improving the quality of HSIs, these technologies mainly focus on recovering an HSI captured in the normal sunlight. It is acknowledged that HSIs are captured via passive imaging mechanisms covering the spectral bands from visible& near-infrared to shortwave infrared spectral range (i.e., around 400nm to 2500nm). The imaging condition limits HSI spectrometers to capture HSIs without sunlight (e.g., in dark environments or night time). In this work, a low-light HSI restoration method is proposed, where we borrow idea of intrinsic decomposition based on Retinex theory in natural image low-light enhancement. Additionally, camera response function that describe the spectral degradation of RGB image and relationship between irradiance and pixel values are employed, respectively. The experimental results validate the effectiveness of the proposed method.
A knowledge graph visualization and retrieval system based on joint extraction model

Knowledge graph is a kind of storage structure which conforms to human cognition form and can accurately describe the relationship information between things. In this paper, a visualization and retrieval system of knowledge graph is built starting with the original Textual corpus. This paper first analyzes the requirements of the visualization and retrieval system. Then in the system implementation stage, in order to realize the visualization and retrieval system, we divide the system into three modules (Data Module, Visualization Module and Query Module) and implement them, respectively. For data module, we based on the entity relation joint extraction model processing text data into relational data (triples). For visualization module, we visualized the triples into node-link diagram based on the force-directed algorithm. Apart from this, we realized some interactive functions based on the D3.js visual framework and HTML technology.
NJUNLP’s Submission for CCMT 2022 Quality Estimation Task

Quality Estimation is a task aiming to estimate the quality of translations without relying on any references. This paper describes our submission for CCMT 2022 quality estimation sentence-level task for English-to-Chinese (EN-ZH). We follow the DirectQE framework, whose target is bridging the gap between pre-training on parallel data and fine-tuning on QE data. We further combine DirectQE with the pre-trained language model XLM-RoBERTa (XLM-R) which achieves outstanding success in many NLP tasks in order to improve performance. With the purpose of better utilizing parallel data, several types of pseudo data are employed in our method as well. In addition, we also ensemble several models to promote the final results.
FASH-IO-N-FWD: Generative Workshop for Computational Fashion Design

This paper outlines an exploration to be carried out by the author, utilizing a constructionist, co-experiential methodology to explore alternative ways in which traditionally trained fashion designers can apply computational methods in their practice. Leaning on Constructive Design Research (Koskinen 2011) traditions, fashion designer participants will be confronted in their practice with knowledge outside their field (i.e. computational design methods). In response, through a process of priming, sensitization and co-design, they will generate novel, alternative approaches to implement computation into their creative processes. The outcomes of this study will be the generated novel, alternative computational design methods which are aligned to the fashion design paradigms, and be utilised for innovating practice and pedagogy, as well as contribute to the development of future computational design tools.
PENGEMBANGAN SILABUS PEMBELAJARAN BAHASA INGGRIS BERWAWASAN EKOWISATA PADA KAMPUNG INGGRIS BERBASIS WISATA NEGERI SAWAI MALUKU TENGAH

This study aims to develop a syllabus of English language learning with an ecotourism perspective in a Tourism-Based English Village in Sawai, North Seram District, Central Maluku Regency. The development research carried out is intended to find out the learning syllabus used in tourism-based English villages in Sawai at this time, obtain data and conclusions about the syllabus used, compile and develop a syllabus that emphasizes the concept of ecotourism according to the needs of students in Kampung Inggris Sawai, obtaining the validity of test results from material, syllabus and curriculum experts, obtaining field test results on the syllabus that has been developed, and obtaining the feasibility results of the syllabus. The method used in this study is Research and Development (R&amp;D). The syllabus development model uses procedures developed by Yalden, namely needs analysis, description of goals, selection of syllabus type development, making proto syllabus, making learning syllabuses, as well as evaluations that include validation from experts and trial stages. The syllabus that has been developed covers topics related to the concept of ecotourism in Sawai, namely environment, Asinahu River, Enjoy Your Stay, Weather and Season, Marine Tourism, Beach Watersport, Transportation, The Beauty of Ora, The Love Pond of Bubara Fish, The Hatupia Cliff, Natural Resources, Festivals in Sawai, Ecotourism, and Local Wisdom. Keywords: English Syllabus, Ecotourism, Sawai, Development
A Study on the Evaluation of Higher Art Education by Optimizing Neural Networks with the Bottleneck Algorithm

In order to increase the quality of education in art and design education, we have developed a system for evaluating the quality of art education from the three sides in order to stimulate teachers’ educational aggressiveness and students’ motivation and motivation. classroom experimental teaching evaluation, internship (sketching, sketching) evaluation and graduation design (thesis) evaluation. The prediction performance of BP neural network is affected by the initial weights and thresholds, and the initial weights and thresholds of the BP model are optimized by the sea-squirt swarm algorithm, and an SSA-BP-based teaching quality evaluation model for art and design majors is proposed.
Current State and Prospects of Increasing the Functionality of Augmented Reality Using Neural Networks

The dynamics of the development of modern society and the rapid breakthrough of the technological component led to the need to interact with fast-changing and client-oriented information in real time. This need is met through the use of augmented reality technology, which allows users to interact in real time with both the real physical and virtual digital worlds. The rapid digitization of human existence has provoked an exponential increase in the amount of existing data, thereby posing new challenges to the scientific community. At the same time, the technology of deep learning, which is successfully applied in various fields, has a rather large potential. The purpose of this study is to present the potential of combining technologies of augmented reality and deep learning, their mutual improvement and further application in the development of modern highly intelligent programs. The work briefly provides an understanding of the concepts of augmented and mixed reality and also describes the technology of deep learning. Based on the literature review, relevant studies on the development of augmented reality applications and systems using these technologies are presented and analyzed. After discussing how the integration of deep learning into augmented reality increases the quality and efficiency of applications and facilitates the daily life of their users, conclusions and suggestions for future research are provided.
Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare

The advent of Federated Learning has enabled the creation of a high-performing model as if it had been trained on a considerable amount of data. A multitude of participants and a server cooperatively train a model without the need for data disclosure or collection. The healthcare industry, where security and privacy are paramount, can substantially benefit from this new learning paradigm, as data collection is no longer feasible due to stringent data policies. Nonetheless, unaddressed challenges and insufficient attack mitigation are hampering its adoption. Attack surfaces differ from traditional centralized learning in that the server and clients communicate between each round of training. In this paper, we thus present vulnerabilities, attacks, and defenses based on the widened attack surfaces, as well as suggest promising new research directions toward a more robust FL.
The rare collection of the Perm Regional Library named after A. M. Gorky: history and contemporary state

The article is devoted to the vectors and marks in the development of the Perm State Regional Universal Library named after A.M. Gorky rare collection for a long historical period: from its foundation in 1836–1935 to the formation of all traditional for rare collections areas of activity. Particular attention is paid to the changes that the rare collection is undergoing in recent years in connection with the introduction of the concept of “book monuments”, the formation of a new approach to conservation, accounting, state registration and promotion of rare collections and book monuments. The author shows the transformation of approaches to the accessibility of rare collections, dwells on the concept of “open rare collection”, which is embodied in the work of the library. Separately, the activities of the Regional Center “Book Monuments of the Perm Territory” are shown, aimed at identifying, cataloging and by the piece scientific description of handwritten books and early printed publications, methodological work on training personnel to work with rare collections and book monuments. The work on presenting book monuments in the “National Electronic Library”, in printed collections and catalogs of individual stocks and collections, on partnership cooperation in implementing exhibition and educational projects of recent years is described.Modern approaches to organizing collections storage, creating exhibition space and work with researchers in the reading room and in the virtual space facilitate preservation of collections, promotion of knowledge about the rare collection of the Perm State Regional Universal Library named after A.M. Gorky and information openness.
Sentiment Effect on Mandiri Bank Stock using Feature Expansion Word2vec and Classification Method Gradient Boosted Decision Tree

In June 2019, there was an accident to the Mandiri bank system. On the same day of the accident, Mandiri bank stock price is decreasing. Many customers use social media as a means of conveying their opinions on this accident, one of the social media used is twitter. Twitter is a service used by users to upload information in the form of text that contains sentiments from its users. However, Twitter itself has a character limit, which is only 280 characters. As a result of character restrictions, opinions expressed may contain vocabulary mismatches because they contain slang, abbreviations, emoji, etc. Because of this, it is difficult to understand the context of the opinions expressed. Therefore, Word2vec feature expansion is used to deal with the problem of vocabulary mismatch, which can result in word context. This study develops a Twitter sentiment classification system using the Word2vec feature expansion method with the Gradient Boost Decision Tree classification algorithm. The results of this study indicate that by using feature expansion in the classification algorithm, model performance has increases to 3.29% for accuracy, 3.35% for precision, 3.31% for recall, and 3.31% for f1-score. From the sentiment classification model built to predict stock price movements, sentiment has no correlation towards stock price movements, with the correlation value of 0.097 for positive sentiment and 0.124 for negative sentiment.