Reviewerly/exHarmony huggingface dataset:
Detecting Inconsistent Vulnerable Software Version in Security Vulnerability Reports

At present, the vulnerability database research has mainly focused on whether the disclosed information is accurate. However, the information differences between the various vulnerability databases have received little attention. This article proposes a WITTY (softWare versIon inconsisTency measuremenT sYstem) to detect the differences between the affected software versions of NVD and different language vulnerability databases (including English CVE, OpenWall, Chinese CNNVD, CNVD, and other eight databases). WITTY can enable Our large-scale quantitative information consistency. We introduce named entity recognition (NER) and relation extraction (RE) based on deep learning. We present custom design into named entity recognition (NER) and relation extraction (RE) based on deep learning, enabling WITTY to recognize previously invisible software names and versions based on sentence structure and context. Ground-truth shows that the system has a high accuracy rate (95.3% accuracy rate, 89.9% recall rate). We use data from 8 vulnerability databases in the past 21 years, involving 554,725 vulnerability reports. The results show that they are inconsistent. The software version is prevalent. The average exact match rate of English vulnerability databases CVE, OpenWall, and other vulnerability databases with cve is only 22.1%. The average exact match rate of Chinese CNNVD and CNVD is 49.5%, and the excat match rate of Russian vulnerability databases is 25.8%.
Means of developing the methodological culture of master degree students

Due to a number of objective reasons (the aging of pedagogical personnel, a request for a new type of lecturer), pedagogical education becomes in demand, the continuity of which is due to the specifics and peculiarities of pedagogical activity. The quality of training for the educational environment is determined by the ability to integrate pedagogical and scientific activities, which is proved by researchers. The nature of professional tasks in any sphere of activity in modern conditions is distinguished by abundance of situations of uncertainty, which requires a special culture of thinking. The article proves, on the basis of the difference between pedagogical activity and scientific activity, the need for master degree students to form the «psychology and pedagogics of professional education» course of methodological culture as a special form of pedagogical consciousness. For gaining an insight into the essence of theoretical pedagogical knowledge, it is necessary to take a certain methodological side, having mastered the scientific methods of understanding pedagogical processes. In the course of masters' methodological culture formation, the importance of the theoretical and methodological base is understood, on the basis of which a comprehensive analysis of the problem is carried out, conceptual ideas are substantiated, educational technologies are found and methodological recommendations are given. The article draws attention to the definition of pedagogical research logic and the level of its methodology adequately to the pedagogical science methodology which serves as a theoretical justification for pedagogical activity. Attention is drawn to the fact that a high level of a lecturer's professional readiness is determined by the availability of methodological literacy which contributes to obtaining reliable new scientific knowledge about pedagogical processes, mechanisms and content. The experience of their scientific preparation is presented, the understanding of which made it possible to develop the concept of the formation of a methodological culture, the dominant of which is the «Methodology and methods of organizing scientific research» discipline. Each of the four stages of the organization is represented by algorithms of methodological characteristics that as a whole determine the logic of scientific search. In accordance with this concept, lecturer-researchers are trained at the level of master's and postgraduate studies at Samara National Research University.
Fine-Grained Double-View Link Prediction Within the Dynamic Interaction Network

Trend prediction in financial trading markets is crucial in decision-making support, normally realized via mining trading patterns or analyzing technique indicators. However, cryptocurrency decentralized exchange (DEX) markets are growing rapidly recently and draw much attention. Different from the centralized market, DEX allows users to observe the whole market data at the order-level, where identity information associated with orders is also available. Moreover, trading pairs in DEX are often manipulated by bots, and user ordering behavior may be affected by these algorithm-controlled bots. These provide us with a novel human-bot game scenario to investigate the predictability of human ordering behavior. In this paper, we model the trading market of the cryptocurrency DEX at each timestamp as a user-order bipartite graph and propose a double-view dynamic network for fine-grained user ordering prediction in a human-bot mixed DEX market. Given the ordering information of the humans and bots, we learn current latent representations of humans and bots using graph neural networks and recurrent neural networks respectively for user ordering prediction. We conduct experiments on real cryptocurrency data from Binance Dex. The experimental results of link prediction demonstrate that our proposed method outperforms the state-of-the-art models.
Application of Intelligent Recommendation Algorithm in Data Mining of Scientific Research Management System

With the improvement of the status of scientific research and the improvement of scientific research level, traditional management methods and methods are becoming more and more difficult to meet the current increasingly complex scientific research management needs. Under the background of the rapid development of information technology, scientific research management informatization has become inevitable choice for future scientific research management innovation. This article aims to study the application of intelligent recommendation algorithm in scientific research management system data mining. Based on the analysis of recommendation system, intelligent recommendation algorithm and system design principles, a scientific research management system based on intelligent recommendation is designed. When it is 15, the minimum support is 120, and the minimum confidence is 0.8, the test effect is the most ideal.
Chosen Plaintext Attack on Jpeg Image Encryption with Adaptive Key and Run Consistency

A JPEG image encryption with an adaptive key generated by a preserved characteristic not only lowers the cost of key management but also enhances the ability to resist various attacks. However, there are still security risks under the chosen-plaintext attack (CPA) conditions. This paper designs a novel CPA scheme to obtain details of the original image. First, the adaptive key can be reproduced from the encrypted image, so that the plaintext images with the same adaptive key can be constructed. Second, the existence of run consistency of MCUs (RCM), which facilitates rapid estimation. In addition, the single permutation for the runs of MCUs with RCM is designed for more accurate estimation. Experimental results show that two plaintext images are sufficient to get the image details. For an image of high quality factors (QFsâ‰¥90) or with rich complex texture, the accuracy ratio of estimated permutation sequence is close to 100%.
Sensitive Data Hiding in Financial Anti-Fraud Process

This research presents an approach to protect personally identifiable information in compliance with the national and European institutional data protection framework in a way that still allows interoperability of information systems and applications.It is proposed to adopt privacy-preserving information hiding techniques to facilitate targeted data mining without infringing privacy restrictions.This approach is proposed as a strategic tool in the fight against financial and insurance fraud.To resolve issues related to the implementation of the protected registration interface process, the research team is turning attention to the development of algorithms and approaches based on intelligent itemset hiding.The research proposal attempts to contribute to the strategic modernisation of public authorities and financial organisations, aiming at the production of original software to provide services to them, facilitating and accelerating the work to combat fraud.The approach is analytically prevailing on previous approaches and it has experimentally shown encouraging results.
New syntactic insights for automated Wolof Universal Dependency parsing

Focus on language-specific properties with insights from formal minimalist syntax can improve universal dependency (UD) parsing.Such improvements are especially sensitive for low-resource African languages, like Wolof, which have fewer UD treebanks in number and amount of annotations, and fewer contributing annotators.For two different UD parser pipelines, one parser model was trained on the original Wolof treebank, and one was trained on an edited treebank.For each parser pipeline, the accuracy of the edited treebank was higher than the original for both the dependency relations and dependency labels.Accuracy for universal dependency relations improved as much as 2.90%, while accuracy for universal dependency labels increased as much as 3.38%.An annotation scheme that better fits a language's distinct syntax results in better parsing accuracy.
Predicting the Future Actions of People in the Real World to Improve Health Management

Busy life makes people do not have time to exercise, stay up late, often eat fast food. Therefore, predicting and recommending what actions should do at what times of day are necessary and also help people improve their health without functional foods or advising of doctors. However, human actions are extremely complex and affected by many subjective and objective factors. The previous proposed methods gradually improve the accuracy of action prediction, but not yet focus on the health-affecting activities and the processed time. Therefore, it is still difficult to apply the predicted results in the reality, such as health app. Therefore, we focus our research on how to solve this problem. Based on user action data, identify attributes related to daily activities that affect health. From there, apply data processing techniques and machine learning methods in the field of data science to solve this problem. In this paper, we propose a PAS-KNNTP (Periodic Action Sequences—K Nearest Neighbor for Time Prediction) method based on personalized key features to predict the next action and when it occurs. To evaluate the effectiveness of the proposed method, we experiment on real-world user action data for the period of 17 months. Experimental results show that the method still ensures the accuracy of predicting future actions, improving the accuracy of predicting the time of happening action up to 20% and reducing the prediction time from 20 min to 15 s. Experimental results prove that our method is capable to apply the reality.
Associative Linking for Collaborative Thinking: Self-Organization of Content in Online Q&amp;amp;A Communities

Virtual collaborative Q&A communities generate shared knowledge through the interaction of people and content. This knowledge is often fragmented and its value as a collective, collaboratively formed product, is largely overlooked. Past research has suggested that the links participants add to associate between content units and to direct others in their navigation can yield a networked formation of the discussion. Inspired by work on the semantic networks of individuals, the current study attempts to explore the role of these associative links in shaping the topology of the discussion network into organizing structures. Network analysis tools were applied to eight Q&A discussions within the Stack Exchange platform, to extract topological indicators of a complex organization. A comparison to simulated random networks similar in size and growth rate revealed that the topological properties of the real discussion networks diverge significantly from what would be expected given random linking. Compared to their random counterparts, the participant-added links led to networks that were more clustered, integrated into larger subgraphs, and included more connected “hubs”. These differences appeared to mostly grow over time. The networks’ largest connected subgraphs were modular, comprising sparsely connected areas with elevated inner connectivity. T he findings indicate that the topology of the discussion graphs encompasses organizing network structures and that these are reliant on the links added by participants. The findings point to the importance of the associative links that are shaping the collective artifact by serving as a latent mechanism for the self-organization of the discussion network.
User Experience Requirements of Digital Moderation Systems in South Africa: Using Participatory Design Within Design Science Research

The digital transformation within the global educational environment led to a transformation in assessment strategies and the increased use of ICTs in assessments. Digitization offers innovative teaching and learning opportunities. But it also introduces new challenges to current quality assurance processes. A narrative literature review revealed a scarcity of empirical evidence on dedicated digital moderation systems and no representation of teachers’ views could be found. The purpose of this paper is to report on an investigation into the requirements for a digital moderation (eModeration) system to enhance teachers’ user experience (UX) of moderation practices in the school environment. Design science research (DSR) is the paradigm applied to guide the alignment of the findings from a literature review on eModeration requirements with the findings from a Participatory Design (PD) intervention for eliciting UX requirements for an eModeration system. The context is school teachers in South Africa. The methodological contribution of this paper is the transdisciplinary approach of using DSR to guide both the literature review and the PD interaction towards a synthesis of the requirements and the implementation in a prototype eModeration system. The theoretical contribution is the evidence based UX design requirements of an eModeration system.
Psychological Analysis of Mathematical Text (Grade 6) Using the Laws of Perception According to M. Wertheimer

The article provides a structural psychological analysis of the correspondence between the structure of two mathematical text (grade 6, by Vilenkin and Zubareva). There were regarded the psychological laws of perception according to M.Wertheimer. The article provides the passage about Rational numbers for a mathematics textbook. The author provides the maximum number of the effects and factors of perception of the educational and scientific text. The purpose of this article is to analyze the school mathematical texts using psychology instruments, to identify the advantages and disadvantages of understanding and perceiving the material. The objectives of the article are the application of methods for analyzing the content, forms and language of presentation of a mathematical educational and scientific text, comparing the results obtained and suggesting an “ideal” one for adequate perception of information by a secondary school student as a subject of his own cognitive reality.
A Distributed Supply Chain Architecture Based on Blockchain Technology

Traditional supply chain management systems use centralized servers and centralized databases, which exhibit disadvantages such as opaque transaction information, difficult information sharing, high maintenance costs, low database security, and poor anti-risk capabilities. Here we develop a supply chain transaction architecture based on blockchain technology. It stores data on decentralized blockchains, and these distributed data are traceable and antitampering under low maintenance costs. We take the alliance chain as the underlying architecture and adopts the “blockchain + business database” double-layer data storage structure to ensure the safety and convenience of transaction data, which will protect the security of transaction data and ensure the speed of data processing. We conduct simulation experiments and performance tests on the system. The experimental results show that the supply chain system using this architecture achieves high efficiency, transparency and security, as well as it could be utilized in the real applications.
Accuracy of Mammography in the Diagnosis of Breast Cancer

Background: Breast cancer is one of the leading causes of cancer-related deaths among women worldwide. Mammography was recently introduced at the Nnamdi Azikiwe University Teaching Hospital (NAUTH), Nnewi, Nigeria for breast cancer screening and diagnosis at the institution. Objective: The objective of this study is to determine the diagnostic accuracy of mammography in the assessment of palpable malignant breast lump among adult females referred from the NAUTH outpatient clinics using histopathology as gold standard. Materials and Methods: This prospective cross sectional study involved recruitment of consenting adult females with palpable breast lump referred for mammography between October 2017 and November 2019 at NAUTH, Nnewi, Nigeria. Two standard views—cranio-caudal (CC) and medio-lateral oblique (MLO)—were done for each patient. Spot magnification or other supplementary views were only done when indicated. Data analysis was done using Statistical Package for the Social Sciences (SPSS) version 21.0 for Windows software. Results: The sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of malignant breast lesions were 41.4%, 98%, 96.7%, 54.4%, and 65%, respectively. The most common features of malignancy were mammographic opacity with an irregular outline (86.7%) and architectural distortion (80%), whereas asymmetric density (10%) was the least common feature. Conclusion: Mammography has a fairly high overall diagnostic accuracy in breast cancer diagnosis. However, an alternative imaging modality is required for dense breasts. This study’s reported specificity (98%) suggests that the test has a very high ability to correctly identify non-cancerous breast lumps. This is particularly relevant in breast cancer screening with an objective to rule out the presence of the disease among adult females in the population.
Utility Preservation of Clinical Text After De-Identification

Electronic health records contain valuable information about symptoms, diagnosis, treatment and outcomes of the treatments of individual patients.However, the records may also contain information that can reveal the identity of the patients.Removing these identifiers -the Protected Health Information (PHI) -can protect the identity of the patient.Automatic deidentification is a process which employs machine learning techniques to detect and remove PHI.However, automatic techniques are imperfect in their precision and introduce noise into the data.This study examines the impact of this noise on the utility of Swedish de-identified clinical data by using human evaluators and by training and testing BERT models.Our results indicate that de-identification does not harm the utility for clinical NLP and that human evaluators are less sensitive to noise from de-identification than expected.
APIs Are the Public Persona of an Industrialised Bank

Existing data and application deployment and integration technology at third-generation banks are not sufficient to handle the expected data volume, data variety coverage, process latency, and agility requirements. What prevents the existing technology from achieving business outcomes is its ability to integrate in an agile way. Agility can be achieved only when integration capabilities are industrialised.
THE LOFTY OBJECTIVE OF THE ACADEMIC PTOFESSION IS TO MOULD A PERSON AND A SPIRIT – MYTH OR REALITY?

The paper studies content components of the concepts "image", "pedagogical image", analyzes professional and personal characteristics of a university teacher based on pedagogical research. Much attention is paid to the definition of the role of a teacher, social significance of his/her profession, influence of a teacher's personality on students’ moral qualities.
Amazon Alexa AI’s System for IWSLT 2022 Offline Speech Translation Shared Task

This paper describes Amazon Alexa AI’s submission to the IWSLT 2022 Offline Speech Translation Task. Our system is an end-to-end speech translation model that leverages pretrained models and cross modality transfer learning. We detail two improvements to the knowledge transfer schema. First, we implemented a new loss function that reduces knowledge gap between audio and text modalities in translation task effectively. Second, we investigate multiple finetuning strategies including sampling loss, language grouping and domain adaption. These strategies aims to bridge the gaps between speech and text translation tasks. We also implement a multi-stage segmentation and merging strategy that yields improvements on the unsegmented development datasets. Results show that the proposed loss function consistently improves BLEU scores on the development datasets for both English-German and multilingual models. Additionally, certain language pairs see BLEU score improvements with specific finetuning strategies.
Analysis of Marketing Strategy and Successful Leadership in a Case of Hyundai Motor Company

Hyundai leads the automobile industry and has made very positive contributions to the overall achievements of economic and social values. Kang et al (2014) pointed In constructing global marketing strategy, Hyundai Motor’s vision is "Lifetime partner in mobility and beyond" and its goal is global top 3 brand by year 2015 through modern premium brand image and selling 5 million vehicles. The target brand positioning of Hyundai Motor is the leading position in premium dimension and stylish/modern dimension. The global brand strategy framework is based on the brand direction of "Modern Premium" and is designed to deliver core brand identity (i.e., Simple, Creative, Caring) to customers. Its marketing strategy shows us: - Hyundai's product strategy is to standardize technical standards with the ambition of being a global product, ensuring the best quality, aiming at the recognition of the value of the customer's product, satisfying the customer's needs. On the other hand, Sale activities is placed at showrooms, though sale agents, and sale person team. Beside, according to the results of quantitative research, in a multi-factor model, we would suggest the government, Ministry of Finance and State Bank: control inflation properly, and firm management control cost properly (as reduction in cost and CPI will cause net profit to increase).
The Impact of Urban Management on Crowd Movement and User Experience through the Pilgrimage of Hajj (Frequent Temporary Mega-Events)

it's clear to the world that the eastern Japan earthquake was the biggest disaster the modern world had ever came to face, that's why it was of great importance that we took crucial lessons of it on every aspect possible so that we can be more ready for future disasters, no matter what is their scale, because as we know disaster are going to come, in all shapes and kinds, and a major disaster like the Great Eastern Japan Earthquake is going to be upon us every few decades, so being ready, as calculations have proofed, spending and providing for the preparedness phase, is 7 times better, safer and cheaper, than trying to solve the problems in the recovery phase, after the disaster happens, in this study we'll be doing an analysis of the event, and suggesting numerous solutions to prevent the likelihood of a similar scenario in the future.