Reviewerly/exHarmony huggingface dataset:
Binary linear codes with few weights from Boolean functions

Boolean functions have very nice applications in coding theory and cryptography. In coding theory, Boolean functions have been used to construct linear codes in different ways. The objective of this paper is to construct binary linear codes with few weights using the defining-set approach. The defining sets of the codes presented in this paper are defined by some special Boolean functions and some additional restrictions. First, two families of binary linear codes with at most three or four weights from Boolean functions with at most three Walsh transform values are constructed and the parameters of their duals are also determined. Then several classes of binary linear codes with explicit weight enumerators are produced. Some of the binary linear codes are optimal or almost optimal according to the tables of best codes known maintained at http://www.codetables.de , and the duals of some of them are distance-optimal with respect to the sphere packing bound.
Spectral collapse in anisotropic two-photon Rabi model

In this communication, based upon a squeezed-state trial wave function, we have performed a simple variational study of the spectral collapse in the anisotropic two-photon Rabi model. Our analysis indicates that the light-matter interaction and the spin-flipping (together with the anisotropy) effectively constitute two competing impacts upon the radiation mode. Whilst the former tries to decrease the radiation mode frequency, the latter may counteract or reinforce it. The light-matter interaction appears to dominate the frequency modulation as its coupling strengths go beyond the critical values, leading to the emergence of the spectral collapse. However, at the critical couplings the dominance of the light-matter interaction is not complete, and incomplete spectral collapse appears. Accordingly, at the critical couplings the eigenenergy spectrum comprises both a set of discrete energy levels and a continuous energy spectrum. The discrete eigenenergy spectrum can be derived via a simple one-to-one mapping to the bound state problem of a particle of variable effective mass in a finite potential well, and the number of bound states available is determined by the energy difference between the two atomic levels. Each of these eigenenergies has a twofold degeneracy corresponding to the spin degree of freedom.
Pengembangan E-Modul Kontekstual Akuntansi Perbankan Syariah Kelas XI Berbasis Flip Pdf Professional

This research aims to develop teaching materials for Islamic banking accounting contextual e-module for class XI semester 1 with the help of professional flip pdf applications. The development of e-modules is based on the challenges of 21st century learning innovation, the absence of technology-integrated learning media so that online learning in the Covid-19 pandemic does not run effectively, and there is no learning media that contains basic competencies coherently. This research uses the Analysis, Design, Development, Implementation, Evaluation (ADDIE) model. Collecting research data using review sheets and validation sheets of experts as well as student response questionnaires. The subjects of this research trial were 10 students of SMK Negeri 2 Mojokerto. The results showed that the product was very feasible. Product eligibility is calculated based on the material feasibility average, graphic feasibility average, and language eligibility average. Student response to the product is very good and very feasible. Based on the results of the feasibility of the products of the experts and the student response tests, it shows that the product is very suitable for use as teaching materials in the 21st century era.
A Study of Machine Learning Algorithms in Speech Recognition and Language Identification System

Speech recognition is a broad topic that primarily involves sub-topics like language identification, speaker identification, speech emotion recognition, speech to text systems, text to speech systems, dialogue systems and much more. While, human beings are quickly able to recognize or identify a language because of the corpora of knowledge built over the years. However, it is a challenging task to have a machine identify a spoken language. So, to build a system that can correctly identify multiple languages irrespective of the dialect and speaker characteristics is an interesting area of research. One benefit of such a LID system is that the barrier between people caused due to language differences will be broken. Such a system will further the progress of globalization. The latest developments of machine learning in speech and language are described as a detailed state of the art in this paper.
Twitter Sentiment Analysis In Diabetes Domain Using Apache Flume And Hive

Twitter is a social media platform used by millions of people around the world. People express their feelings by posting tweets related to various topics and products. Sentiment Analysis on these tweets can be performed to analyse their opinion. This paper endeavours to perform Sentiment Analysis by extracting tweets related to diabetes domain from twitter.com via Apache Flume and store them in JSON format. By using Apache Hive, the tweets are transferred from the text file to a table and are analysed by comparing the sentiments expressed in the tweets with the AFINN dictionary. Each individual tweet is scored based on a scale from -5 to +5, where a score having value less than zero indicates a negative sentiment; a zero indicates a neutral sentiment, and a score which has a value greater than zero indicates a positive sentiment. This study can benefit the people suffering from diabetes by making them aware about diet, lifestyle and precautionary measures required to manage their condition in a better way. Also the heath care organizations can utilize the results of this research and improve their strategies to benefit the society.
A Literature Review on Quality Prediction in Data Warehouse

The data warehouse has been proved to be of utmost importance in managing large data of an organization and help managers and business analysts in making organization-wide decisions. Information in the storehouse should exist in a high-grade state as low-quality facts and data may lead to wrong strategic decisions. A bad quality in the warehouse of data can lead to adverse results from both industrial and professional point of view. Various factors for quality prediction have been undertaken in this study. It has been found that the existing systems have various gaps that need to be addressed. Thus, a novel system is proposed in the study so as to predict the qualitative aspect of the warehouse.
The Pedagogical Strategies for Online Music Learning of Instrumental Lessons in Higher Education

With the increasing growth of modern technology, more and more educators incorporate innovative equipment in their classes for advanced educational approaches.Particularly, the physical courses are limited or prohibited during the COVID-19 pandemic, and therefore the online courses became mandatory for the schools.The online course provides a different experience from the physical class, but in the meanwhile, it takes more energy and preparation for educators to manage the class.Music learning in higher education is a multidisciplinary process that associates with various aspects of history, literature, fine arts, social culture, and psychology.The distance learning of instrumental lessons has displayed several problems and challenges compared with the traditional learning pathways.In order to make a better course delivery, this study will propose the pedagogical strategies for online instrumental music learning in higher education.
Design of Automatic Answer Checker

In a world where technology has grown to be a mega thing impacting the lives of mankind to a greater extent, a common man is still having huge difficulty in adapting the available technical solutions immediately. There are so many basic tasks that are performed in the day-to-day lives of people which take a lot of effort and time and can be automated with the help of technology but still, and humans are more prone to do them manually. One such example is taking subjective examinations by students and then evaluation of the answer sheets manually by the teachers. So, to ease the process of subjective answer checking, a web application has been developed that can check the subjective answers written by the students with the same efficiency as in the case of manual checking by the teachers. Using this software, teachers can set the question paper and students can take their examination by getting their answers checked simultaneously. The model’s development is settled with the Word2Vec skip-gram model, Natural Language ToolKit (NLTK) modules, and Flask Python framework, whereas all the data have been stored in the form of relational database tables which are inserted and fetched through the SQL queries. The Word2Vec skip-gram model is used to make the vocabulary of terms and to find their corresponding word embeddings (feature vectors). The paper proposes the development of a model that will make a time-consuming and tiresome task like manual answer checking as effortless as possible.
Implementation Of Tutor Support In Preparation Of Educational Managers.

The article discusses issues related to the organization of productive interaction between a teacher and students in the framework of professional training of educational managers through tutoring.The specifics of this interaction and the ways of its organization are revealed, the essential characteristic of these concepts and all related tutoring segments is determined.Concepts, such as "organization of interaction", "productive interaction", "design", "project teams", "project skills and methods", etc. are used in this work, the specifics of the above concepts are considered, their most important features in the context of the selected topic are highlighted.It is significant for the authors, that the collective of students is a complex structure that requires special attention both to the integrated complex and to its individual segments, especially within the framework of such a complex and complicated area of training as educational management.In addition, the main conceptual result of the study is the analysis of the questionnaire conducted on the basis of one of the federal universities of the Russian Federation -FSAEI HE "V.I.Vernadsky Crimean Federal University" on the topic "Assessing the quality of the organization of productive interaction in student project teams", among the students of various forms and areas of training.The study revealed the advantages of project teams over spontaneous groups of students, and the degree of importance of tutoring for each design subject.
Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]
COVID-19 conscience tracing: mapping the moral distances of coronavirus

One of the many problems posed by the collective effort to tackle COVID-19 is non-compliance with restrictions. Some people would like to obey restrictions but cannot due to their job or other life circumstances; others are not good at following rules that restrict their liberty, even if the potential consequences of doing so are repeatedly made very clear to them. Among this group are a minority who simply do not care about the consequences of their actions. But many others fail to accurately perceive the harms that they might be causing. One of the main reasons for this is that the harms done by transmitting COVID-19 to someone else are morally distant from the agent, particularly in cases where infection is asymptomatic. In this paper, I describe seven different aspects of moral distance in the context of COVID-19, explore how they affect (lack of) motivation to obey restrictions, and suggest several ways in which such moral distance can be reduced — primarily through enhanced-contact tracing that makes it clear to individuals and the public precisely who they could be harming and how.
Automatic Anomaly Detection with Machine Learning

This presentation will discuss the following topics: Understanding the benefits of automatic anomaly detection Identifying precursors with algorithms. Overcoming the challenges of data issues in automation.
Performance Exploration of Hadoop in Fully-Distributed and Pseu-do-Distributed Method

This electronic We propose to analyze the exhibition of Big Data applications with Hadoop and along these lines to make sense of the presentation in data security. Ha-doop is formed on MapReduce, one of the generally uti-lized programming models in Big Data. This paper aims to evaluate performance factors and compare the con-ventional method of providing security and distributed method of providing data security. This assessment will set up the fundamental abilities that ought to be consid-ered explicitly on a Distributed File System (HDFS: Ha-doop Distributed File System) for execution as far as speed. Large information has explicit attributes (Volume, Velocity, Value, Variety, Veracity - 5V) that make it hard to oversee from a security perspective. Consequently, we propose a model that gives enormous information secu-rity using Hadoop and Pig-Latin and analyze the afteref-fects of the traditional method and distributed method. Through this work, it was expected that Hadoop and Pig-Latin are skilled in Data Security
Keynote: Transparent, Trustworthy and Privacy-Preserving Supply Chains

Over the years, supply chains have evolved from a few regional traders to globally complex chains of trade. Consequently, supply chain management systems have become heavily dependent on digitisation for the purpose of data storage and traceability of goods. However, current approaches suffer from issues such as scattering of information across multiple silos, susceptibility of erroneous or untrustworthy data, inability to accurately capture physical events associated with the movement of goods and protection of trade secrets. Our work aims to address above mentioned challenges related to traceability, scalability, trustworthiness and privacy. To support traceability and provenance, a consortium blockchain based framework, ProductChain, is proposed which provides an immutable audit trail of product's supply chain events and its origin. The framework also presents a sharded network model to meet the scalability needs of complex supply chains. Next, we address the issue of trust associated with the qualities of the commodities and the entities logging data on the blockchain through an extensible framework, TrustChain. TrustChain tracks interactions among supply chain entities and dynamically assigns trust and reputation scores to commodities and traders using smart contracts. For protecting trade secrets, we propose a privacy-preservation framework PrivChain, which allows traders to keep trade related information private and rather return computations or proofs on data to support provenance and traceability claims. The traders are in turn incentivised for providing such proofs. A different privacy-preservation approach for decoupling the identities of traders is explored in TradeChain by managing two ledgers: one for managing decentralised identities and another for recording supply chain events. The information from two ledgers is then collated using access tokens provided by the data owners, i.e., traders themselves.
A Research Paper on Web Development Using HTML5 and CSS3

Abstract The future of web development is the HTML and CSS language. HTML5 is a new standard in every place for web users. CSS3 is a modern standard, which exemplifies the record in HTML5 language. In this paper, we are providing knowledge about the HTML5 and CSS3 languages. We also discuss the strategy and approach for web development. This paper tells about the various type of technology used in web development and understanding the methodology to describe the objectives, scope, architect and applications with the brief modification analysis of front-end web development. Keywords : CSS, HTML5, web modification, web technology Cite this Article Mohit Bajpai, Ayush Jain, Chandan Kumar, Bhawani Shankar Purohit. A Research Paper on Web Development Using HTML5 and CSS3. Journal of Advanced Database Management & Systems. 2019; 6(3): 1–4p.
Ore value chain modeling and cost analysis based on Petri nets

ore value chain modeling and cost analysis based on petri nets introduction
Inferring Morphological Complexity from Syntactic Dependency Networks: A Test

Research in linguistic typology has shown that languages do not fall into the neat morphological types (synthetic vs. analytic) postulated in the 19 th century.Instead, analytic and synthetic must be viewed as two poles of a continuum and languages may show a mix analytic and synthetic strategies to different degrees.Unfortunately, empirical studies that offer a more fine-grained morphological classification of languages based on these parameters remain few.In this paper, we build upon previous research by Liu & Xu (2011) and investigate the possibility of inferring information on morphological complexity from syntactic dependency networks.Inferring morphological complexity from syntactic dependency networks: a test
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning.Existing graph-text paired datasets typically contain small graphs and short text (1 or few sentences), thus limiting the capabilities of the models that can be learned on the data.Our new dataset WikiGraphs is collected by pairing each Wikipedia article from the established WikiText-103 benchmark (Merity et al., 2016) with a subgraph from the Freebase knowledge graph (Bollacker et al., 2008).This makes it easy to benchmark against other state-of-the-art text generative models that are capable of generating long paragraphs of coherent text.Both the graphs and the text data are of significantly larger scale compared to prior graph-text paired datasets.We present baseline graph neural network and transformer model results on our dataset for 3 tasks: graph → text generation, graph → text retrieval and text → graph retrieval.We show that better conditioning on the graph provides gains in generation and retrieval quality but there is still large room for improvement. 1
Video Steganography: A Survey of Techniques and Methodologies

In recent decades, steganography has gained momentum due to its ability to communicate data covertly. Steganography embeds secret data into cover medium that are of many forms like text, image audio and video. Video steganography is drawing more impulse due to its scope of masking larger amount of secret information. This paper furnishes the leading-edge techniques and methodologies concerning video as covert communication channel. Both processed and un-processed videos are considered for surveying. Further the feasibility of encryption techniques are also discussed. This paper provides an overall view about the embedding and encryption techniques considering video as cover. Here different steganographic methodologies is divided as early embedding, intermediate embedding and delayed embedding which is based on the time data embedding is done.
Single Low-Light Ghost-Free Image Enhancement via Deep Retinex Model

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions.The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing.In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization.The first three parts can reconstruct the enhanced image that suffers from lowresolution.To reduce the noise of the enhanced image and improve the image quality, a superresolution algorithm based on the Laplacian pyramid network is introduced to optimize the image.The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations.Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm.Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details.Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.