Reviewerly/exHarmony huggingface dataset:
A Mathematical Model of Sound Pressure Attenuation for Optimizing Distribution of Orchestra Instruments

The proper position of the orchestra instruments is important for the sound performance. To optimize the distribution of orchestra instruments, we propose a mathematical model of sound pressure attenuation. To better optimize the orchestra instrument distribution. The algorithm takes the sound pressure attenuation and the air absorption coefficient into account. In addition, we develop a system to test its performance. By choosing the instruments and the sound pressure level inputted by the user and the sound pressure attenuation equation, the optimal distance of the mike from each instrument can be calculated automatically. By doing so, the best distribution plan can be attained.
Analisis Kohesi Leksikal dan Gramatikal Serta Koherensi dalam Teks Diskusi

&lt;p&gt;Writing discussion text is a skill that must be mastered by junior high school students. However, in writing discussion texts the connectedness and linkage of sentences has not received serious attention, especially the use of lexical and grammatical cohesion and coherence. In this regard, the purpose of this study was to find out the inappropriate use of lexical and grammatical cohesion and coherence in the discussion texts of SMP Negeri Jakarta Timur students. This research deals with the object of study of the discussion texts of class IX students of State Middle School in East Jakarta as many as 602 texts. By random sampling, 64 discussion texts were selected and 585 sentences were collected. This is done by analyzing each sentence in the discussion text regarding the inappropriate use of lexical and grammatical cohesion and its coherence. Data were analyzed using a qualitative description method. The instrument used in this study is the human instrument. The results of this study indicate that the use of markers of lexical and grammatical cohesion and coherence is incorrect in 112 sentences or 19.15%. The use of lexical cohesion markers includes the use of repetition of 9 sentences or 1.53%, synonyms of 2 or 0.34%, antonyms of 1 sentence or 0.17%, and collocation of 2 sentences or 0.34%. The use of grammatical cohesion markers includes the use of references in 21 sentences or 3.59%, ellipsis 10 sentences or 1.71%, and conjunctions 33 sentences or 5.64%. The use of coherence markers includes the use of causal relations 6 sentences or 1.03%, affirmative relations 2 sentences or 0.34%, summation relations 5 sentences or 0.85%, conditional relations 2 sentences or 0.34%, relations purpose-conditions 2 sentences or 0.34%, contradictory relations 2 sentences or 0.34%, overgeneralization 2 sentences or 0.34%, and paraphrase relations 10 sentences .or 1.71%.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;Keywords: Lexical Cohesion, Grammatical Cohesion, Coherence, Discussion Text&lt;/p&gt;si
Improved encryption schemes based on McEliece scheme using polar codes

In this paper, we design two public key encryption schemes based on polar codes. Firstly, we propose a direct modification of the McEliece scheme. We use the added error vector to carry additional information bits. Theoretically, the rate of this scheme can be increased to one bit/channel use. However, due to carrying more information, the security of the system is less guaranteed. To address this issue, we propose a new variant of the McEliece scheme, which replaces the "XOR" operation between the codeword and the noise vector with addition in the real number filed, i.e., a two-user binary erasure-multiple access channel (BE-MAC) is formed in the encryption. During the research on BE-MAC, we propose a construction method to enhance the decoding performance. To validate the security and efficiency of these encryption systems, we conduct numerous experiments and simulations. The results demonstrate that scheme 2 has good performance in terms of both safety and efficiency.
Cloud Security Metrics and Measurement

This research aims to investigate the cloud security metrics by exploring the existing frameworks, integrated approaches and quantitative measurements to enhance overall security in cloud environments. This study explains several established frameworks and standards, such as the CSA Cloud Controls Matrix, NIST SP 800-53 and ISO/IEC 27001, which are important in guiding organizations towards strong security practices. Moreover, a qualitative research method has been used in this research study, conducting a literature review of past studies on the same topic. The challenges and limitations regarding cloud security have also been mentioned in this research study, which is helpful for future researchers to explore in detail and develop stronger strategies for enhancing cloud security as a whole. This research study states that the emerging threats and technologies related to cloud security must be explored continuously to develop a detailed understanding of cloud security measures.
Named Entity-Oriented Sentiment Analysis with text2text Generation Approach

Named Entity-Oriented Sentiment Analysis with text2text Generation Approach.
Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing

The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence. However, the KT task suffers from data sparsity, which makes it challenging to learn robust representations for students with few practice records and increases the risk of model overfitting. Therefore, in this paper, we propose a Cognition-Mode Aware Variational Representation Learning Framework (CMVF) that can be directly applied to existing KT methods. Our framework uses a probabilistic model to generate a distribution for each student, accounting for uncertainty in those with limited practice records, and estimate the student’s distribution via variational inference (VI). In addition, we also introduce a cognition-mode aware multinomial distribution as prior knowledge that constrains the posterior student distributions learning, so as to ensure that students with similar cognition modes have similar distributions, avoiding overwhelming personalization for students with few practice records. At last, extensive experimental results confirm that CMVF can effectively aid existing KT methods in learning more robust student representations. Our code is available at https://github.com/zmy-9/CMVF.
Answering Subjective Induction Questions on Products by Summarizing Multi-sources Multi-viewpoints Knowledge

This paper proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to ‘whether the phone is heavy’ has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone. That is quite different from the traditional QA task, in which the answer to a factoid question is unique and can be found from a single data source. To address this new task, we propose a three-steps method. We first retrieve all answer-related clues from multiple knowledge sources on facts and opinions. The implicit commonsense facts are also collected to supplement the necessary but missing contexts. We then capture their relevance with the questions by interactive attention. Next, we design a reinforcement-based summarizer to aggregate all these knowledgeable clues. Based on a template-controlled decoder, we can output a comprehensive and multi-perspective answer. Due to the lack of a relevant evaluated benchmark set for the new task, we construct a large-scale dataset, named SupQA, consisting of 48,352 samples across 15 product domains. Evaluation results show the effectiveness of our approach.
Prioritising Interactive Flows in Data Center Networks With Central  Control

Data centers are on the rise and scientists are re-thinking and re-designing networks for data centers. The concept of central control which was not effective in the Internet era is now gaining popularity and is used in many data centers due to lower scale of operation (compared to Internet), structured topologies and as the entire network resources is under a single entity's control. With new opportunities, data center networks also pose new problems. Data centers require: high utilization, low median, tail latencies and fairness. In the traditional systems, the bulk traffic generally stalls the interactive flows thereby affecting their flow completion times adversely. In this thesis, we deal with two problems relating to central controller assisted prioritization of interactive flow in data center networks. Fastpass is a centralized "zero-queue" data center network. But the central arbiter of Fastpass doesn't scale well for more than 256 nodes (or 8 cores). In our test runs, it supports only about 1.5 Terabits's of network traffic. In this work, we re-design their timeslot allocator of their central arbiter so that it scales linearly till 12 cores and supports about 1024 nodes and 7.1 Terabits's of network traffic. In the second part of the thesis, we deal with the problem of congestion control in a software defined network. We propose a framework, where the controller with its global view of the network actively participates in the congestion control decisions of the end TCP hosts, by setting the ECN bits of IPV4 packets appropriately. Our framework can be deployed very easily without any change to the end node TCPs or the SDN switches. We also show 30x improvement over TCP cubic and 1.7x improvement over RED in flow completion times of interactive traffic for one implementation of this framework.
League of Legends: Real-Time Result Prediction

This paper presents a study on the prediction of outcomes in matches of the electronic game League of Legends (LoL) using machine learning techniques. With the aim of exploring the ability to predict real-time results, considering different variables and stages of the match, we highlight the use of unpublished data as a fundamental part of this process. With the increasing popularity of LoL and the emergence of tournaments, betting related to the game has also emerged, making the investigation in this area even more relevant. A variety of models were evaluated and the results were encouraging. A model based on LightGBM showed the best performance, achieving an average accuracy of 81.62% in intermediate stages of the match when the percentage of elapsed time was between 60% and 80%. On the other hand, the Logistic Regression and Gradient Boosting models proved to be more effective in early stages of the game, with promising results. This study contributes to the field of machine learning applied to electronic games, providing valuable insights into realtime prediction in League of Legends. The results obtained may be relevant for both players seeking to improve their strategies and the betting industry related to the game.
Development of a Morphological Analyser for Siberian Ingrian Finnish

This paper presents our work on the development of a morphological analyzer for Siberian Ingrian Finnish. Siberian Ingrian Finnish is a low-resource language. In this paper, we present an algorithm for analyzing nouns of Siberian Ingrian Finnish and show an example of analysis.
Generative Question Answering Systems over Knowledge Graphs and Tex

In this paper we describe a generative question answering system which relies on text or knowledge graphs to find supporting evidence. The goal of generative QA is to provide a natural full sentence answer relying on the relevant evidence. Unlike existing models, the system proposed here can generate full answers using knowledge base triplets as evidence and is not restricted to simple questions consisting of one triplet. The generation module is a pretrained encoder-decoder transformer. Additionally, we constructed a new dataset DSberQuAD to train and evaluate the generative QA system in Russian. The new dataset was constructed in a rule-based manner and is an extension of SberQuAD with full sentence answers for each question. The proposed model is a new SOTA for Russian KBQA on RuBQ2.0 dataset. All the code and data from this project are be available on GitHub 1 under Apache license.
Exploring Evaluation Techniques in Controlled Text Generation: A Comparative Study of Semantics and Sentiment in ruGPT3large-Generated and Human-Written Movie Reviews

The paper describes the proposed strategy for evaluation controlled text generation with the sentiment as attribute. Our approach mainly consists of automatic sentiment analysis (ruBERT) and topic modelling (BERTopic), which are applied to a parallel corpus with artificially produced and human-written texts. The model for evaluation is fine-tuned on the parsed reviews from big Russian movie-related website ruGPT3Large with the sentiment as prompt. The results of the analysis demonstrate that the proposed methods can offer a more comprehensive understanding of the advantages and limitations in the context of semantics and sentiment. Additionally, the paper employs metrics such as BERTscore and self-BLEU to further evaluate the generated text. The proposed methodology provides a novel approach for evaluating the quality of generated text and may have implications for future studies in the field
Pre-editing Strategy Based on Automatic Evaluation of Translation Complexity to Improve the Quality of Specialized Texts Machine Translation into English

The study addresses the issue of applying optimizing pre-editing of Russian-language texts in order to improve the quality of machine translation into English. A probabilistic assessment of translation task complexity is proposed to be used for selecting a pre-editing strategy. A generalized model of the translation process is presented. A mathematical model and algorithm for automated assessment of translation task complexity are proposed. Test of the model on specialized texts of oil and gas industry is described, which showed that the estimate correlates with an estimate of translation quality and can be used in selecting a strategy for optimizing pre-editing of source texts in machine translation tasks.
Binary classification model as a tool to detect sentences with microsyntactic units

We consider a model of binary classifier predicting occurrence of microsyntactic units in sentences. The model is based on AWD-LSTM architecture with an encoder pre-trained on the Russian version of Wikipedia and further trained on a dataset built from the SynTagRus corpus supplied with a microsyntactic markup. We present the structure of the model and discuss its output. The study showed that binary classification allows targeting of microsyntactic markup and helps to significantly improve its recall.
Association Rules Mining for Designing Product Bundling Strategy (Study Case at a Cafe in Jakarta)

Cafe X is a modern and prominent coffee shop in town that sells specialty beverage products with a total of 73 variations. Sales transactions that occurred in March 2021 at that Cafe reached an average of 300 times per day. All this time, the transaction data is only used to calculate inventory replenishment and to observe sales profits whereas it can actually be utilized to reveal information related to customer behavior in buying products by finding the association or pattern between product sales. The purpose of this study is to obtain association models between items that can be used as a base to arrange recommendations for selling beverage products at Cafe X using a data mining method, namely the association rules with the Apriori algorithm. The transaction data specified for data processing are payment transactions of a total of 3,100 data records. From the analysis results, it is known that there are 8 product association rules that meet the minimum support of 0.01 and the minimum confidence of 0.5, which means that these rules can be used as an alternative basis for determining business strategy at Cafe X. Based on the association information between products, the customer relationship management (CRM) strategy that can be applied at Cafe X is the promotion strategy of product bundling. From a number of recommended bundled products, it is found that there are opportunities to increase sales for four products, they are MD001, TH002, LB001, and TU001.
Implementation of Warehouse Receipt System in Food Warehouses in Kudus District

Agriculture is a sector that is able to score growth during the pandemic crisis. The export performance of the agricultural sector also continues to show positive growth. For this reason, real support is needed so that agriculture can continue to grow sustainably. One of them is by building a modern agricultural commodity product storage system, one of which is by using a warehouse receipt system. The warehouse receipt system has very strategic functions, including as a stock management instrument, logistics financing instrument, and supporting food security efforts. Unfortunately, the utilization of the warehouse receipt system in Kudus Regency is still very low, as indicated by the minimal utilization of the warehouse receipt system. This research aims to analyze the reasons why the warehouse receipt system has not been optimally utilized by farmers. This research uses a qualitative method with Miles and Huberman's interactive data analysis, namely: data collection, data reduction, data display, and conclusion drawing/verification. &#x0D; The results of this study indicate that SRG has great potential to improve the welfare of farmers and advance the agricultural sector in Kudus Regency. However, to overcome various obstacles that hinder the implementation of SRG, serious efforts are needed from various parties, namely: Increase public knowledge and awareness about SRG; simplify the process and requirements for using SRG; improve SRG infrastructure, including information systems and warehouses; improve the ability of farmers to obtain financing through SRG; and improve cooperation between related agencies.
Consumption Habits and Attitudes Associated with Vending Machines Use on a Saudi Arabian University Campus

Current university campus food environments, especially vending machines, include more unhealthy foods and beverages than healthier alternatives, leading to increased consumption and frequent snacking. This study examines the relationship between food and beverage consumption and vending machines, evaluates the available food and beverage types, and investigates students’ behaviors and attitudes associated with vending machines. This cross-sectional study was conducted at King Abdulaziz University in Jeddah, Saudi Arabia, and included female and male students aged 18–26 years. Most participants reported changes in weight (57%) and food habits (56%) in the last month. The most frequently purchased vending machine items were water, chips, and chocolate. Males reported consuming chips, soda, and iced tea more frequently than females. Over 60% suggested fresh salad, yogurt, granola, protein bars, and baked bread as healthier food options in vending machines. These findings can be used to develop targeted initiatives to improve the nutritional quality of vending machine products and encourage healthy food choices.
FORMATION, STUDY AND PUBLICATION OF THE DOCUMENTARY COLLECTION OF THE K. G. PAUSTOVSKY MUSEUM

The collection of K. G. Paustovsky’s museum is the unique and the largest collection of memorial things, manuscripts, letters, documents and photos of the writer. The quantity of units of storage of a documentary fund of a museum is 5 616 units of pieces, among which: manuscripts of Paustovsky’s works - 348 units of pieces. Paustovsky’s letters - 707 units of stock; letters to Paustovsky - 1 252 units of stock; documents of Paustovsky - 121 units of stock, Paustovsky’s diaries - 110 units of stock. The documentary collection of K. G. Paustovsky’s Museum requires serious scientific study. Many documents of the collection became known and accessible to researchers during last 10 years. Without inclusion of documents of a museum collection in an actual scientific turn, it is impossible to study the literary heritage and biography of the writer at the present stage.
THE MUSEUM AS A PUBLISHER OF DOCUMENTARY HERITAGE OF K. G. PAUSTOVSKY

Exhibitions for the Museum of K. G. Paustovsky are the prime means of publishing items from the museum collection. In recent years, the scientific and exhibition activities of the museum can be noted a number of interesting and in their own way unique exhibition projects implemented in the space of the museum and on sites outside the museum, among which are the exhibitions “Konstantin Paustovsky. Uncut” (to the 125th anniversary of K. G. Paustovsky), “Dear brother” (to the 150th anniversary of the birth of I.A. Bunin), “Interlocutors of the heart. Konstantin Paustovsky and Russian writers” and others.
Medical Insurance Fraud Detection Using Machine Learning

Medical insurance fraud poses significant challenges to the healthcare industry, impacting financial resources and patient care. This research explores the application of machine learning methodologies to detect fraudulent activities within healthcare insurance claims. Medical insurance fraud detection is crucial to help insurance companies save money. Machine learning is a powerful tool that can be used to detect fraudulent activities in the healthcare industry. Fraud can be spread broadly and extremely costly to the therapeutic protection framework. Protection can be made unscrupulous and be a case designed to hide or alter such information meant for social insurance benefits. Cheats might be numerous and submitted by the protection guarantor or the safeguarded. The unscrupulous social insurance providers are the reason for extortion in the well-being segment. This research approach is to apply machine learning to find incidents of medical insurance fraud automatically. In conclusion, machine learning is a promising tool for detecting medical insurance fraud. It can help insurers detect fraudulent activities in real time, saving money on bogus claims.
Histopathological Diagnosis and its Pitfall: Bangladesh Perspective

Abstract not available Mugda Med Coll J. 2022; 5(2): 54
A Netnography Analysis of Thematic ­Hadith Memes Distribution on ­­ Social Media

Instead, the use of hadith memes among Muslim communities is rampant, and the study of hadith memes is scarce. This study, therefore, aims to examine the use of hadith memes by millennial Ulama and the types and content of hadith memes disseminated and exchanged in the virtual world by millennial Ulama. This study built thematic hadith memes based on their contents and formats on social media platforms using Rajan's (2015) theory of religious memes. This study employed a netnography method, collecting data from the social media sites Facebook and WhatsApp of Islamic millennial Ulama. We used a thematic approach in analyzing the data. Our results show that hadith memes disseminated on social media sites fall into four themes. The first theme is prayer hadith memes that present the traditions of prayers in Islam. The second theme is hadith memes related to sacrifice, which depict messages of Muslims' willingness to practice gratitude and to sacrifice to do things according to Allah's will. The third theme of the hadith memes is that they present messages about the afterlife, which tells about the end of life, such as life in the grave, hell, and heaven. The last theme is hadith memes about tolerance, which tell about the importance of living in tolerance and respecting each other. We conclude that our study contributed to understanding the distribution of hadith meme themes in the virtual world to enhance the sharing and exchange of Islamic teachings.