Reviewerly/exHarmony huggingface dataset:
Assessment on Recurrent Applications of Machine Learning and its Behaviours

Artificial intelligence (AI) and machine learning are at the moment measured to be the unique widespread inventions. Artificial Intelligence rummage-sale to stand an unbelievable conception from science fiction, but nowadays it’s flattering a day-to-day authenticity. On the other hand, a neural network emulates the procedure of actual neurons in the brain that are parquet the track near innovations in machine learning, baptised deep learning. Machine learning can cosiness us living cheerier, improved, and additional dynamic conscious, if the power of the Deep learning concepts and its proper utilization as an industrial revolution that harness mental and cognitive ability. Currently lots of research papers deal with the Artificial Intelligence of deep learning in various real time applications that includes intelligent gaming, smart driving, and environmental protection and so on. Irrespective of all applications an intelligent decision making must be done timely to improve the accuracy in one end and simultaneously on the other end to consume energy and system efficiency. This paper presents the various applications using deep learning efficiently by better decision making and also how to visualize the problems in order to take a conclusion for better solution. The analysis of such real time problems is done by logically in the form of using artificial neurons through supervised and unsupervised data.
ANALYZING USAGE AND EFFECTIVENESS OF E-LEARNING APPLICATIONS AMONG RURAL AND URBAN STUDENTS: AN EMPIRICAL STUDY

Although Information communication technology was bound to transform teaching learning pedagogy in developing nations, advent of Covid-19 and subsequent nation-wide lock down has acted as a catalyst for the process. Experience from developed world has shown that Information Communication Technology enhances learning process. In India there has always been a prevalence of dichotomy of resources between rural and urban regions. Government of India has taken initiatives like Provision of Urban Amenities to Rural Areas, Pradhan Mantri Jan Dhan Yojana, Deendayal Upadhyaya Gram Jyoti Yojana, Pradhan Mantri Sahaj Bijli Har Gar Yojana to strengthen socio economic infrastructure in rural area. Along with this some of the State Governments like that of Karnataka and Uttar Pradesh have initiatives to promote digital learning among students in both urban and rural areas. In this context it becomes pertinent to analyze whether students in rural area are on par with the students in urban area to access digital learning. This is even more relevant as nearly 65% of Indian population resides in rural areas. Our research paper aims to suggest appropriate policy suggestions to bridge this gap, if any.
Public Health Information Standards

Standards are one of the most efficient ways to prevent data silos, achieve system interoperability, and promote the value of data. Electronic data interchange continues to increase in importance for all health systems, including public health systems. Public health has become a significant participant in health data standards discussions, and must continue to adopt and promote standards, and to participate in standards development as a fully-engaged partner. Public health’s wide variety of partners lends challenges to adoption of standards. Its many partners must agree upon both the selection and value of standards in order to overcome significant barriers. Implementation of standards is complex and resource-intensive, sometimes unevenly more so for one of the partners involved in data interchange. In this chapter, standards are categorized into process standards and data or content standards. After reviewing some common standards utilized in public health, three of the most prevalent—HL7®, LOINC®, and SNOMED CT®—are explored in greater detail.
Boltzmann Machines

In this chapter, you will learn about Boltzmann machines and how the restricted Boltzmann machine can be used to perform anomaly detection.
Diminishing Spread of False Message in Twitter using Block chain and Machine Learning

A communication platform such as social media often works to change or influence opinions when it comes to political views. Microblogging platforms such as Facebook, Pinterest, Tumblr, Instagram enable users to con- tribute new postings which are publicly visible. Twitter stood out as over- powering news equipment, giving information to the world. It is very common for users to logo on to this site for immediate update on recent happenings like natural disasters, political events etc. Due to the enlarging reputation of Twitter, this aroused spammers to circulate false information. The proposed scheme seeks to reduce the spread of false information in Twitter using blockchain consensus algorithm, along with classification and machine learning algorithms. A "tweet" gets posted based on the validation provided by the nodes in the blockchain. The system works according to the sensitivity of the tweet.
An Adaptive Secret QR Code Message Passing Method Using Video Steganography

Steganography is covert communication between the sender and the intended receiver with no evidence to the outer world that the communication has happened. The paper proposes an multi-layer protection approach in steganography to adaptively embed the message in the cover medium and exploit the efficiency of the Diffie-Hellman key exchange method to be passed on to a secure channel and to be extract the secret data without being detected. Hence the proposed system has a shade of hybrid crypto stego scehme. The study proposes a new and adaptive steganography method that provides multiple layers of security and adaptiveness in passing secret data which emphasizes on Imperceptibly and robustness.
Scalable Statistical Introgression Mapping Using Approximate Coalescent-Based Inference

Recent advances in biomolecular sequencing have revealed the important role that interspecific gene flow has played in genome evolution throughout the Tree of Life. Current and future genomic studies will bring large amounts of genomic sequence to bear upon this topic, and scalable computational methodologies are needed to detect and analyze genomic signatures of interspecific introgression in large-scale datasets. To address the methodological gap, we introduce a new computational framework known as PHiMM (or "fast PhyloNet + Hidden Markov Model"). PHiMM combines inference and learning under a combined model of genetic drift, substitutions, recombination, and gene flow with a coalescent-based approximation technique. We compare the performance of PHiMM against the state of the art using synthetic and empirical genomic sequence data. We find that PHiMM offers better computational runtime and main memory usage by multiple orders of magnitude, while returning comparable inference accuracy. An open-source software implementation of the PHiMM framework and open data are publicly available at https://gitlab.msu.edu/liulab/phimm-dataset.
Application of Feature Weighting for the Intensification of Data Classification

Classification is the supervised learning technique which is applied in many of the real time applications. In this study we have considered three classifiers which are widely used and then the intensification of the classifiers are considered. Among various methods to improve the performance of the classifiers, this research concentrate on the feature weighting techniques applied for the classifiers. This analysis is done based on the results obtained from the Rapidminer tool. Here we have deployed four feature weighting techniques for the intensification of the three classifiers. It is tested with three dataset. The experimental environment and the results are discussed in detail.
Enhancing 3-D Spatial Skills of Engineering Students Using Augmented Reality

3D spatial visualization and reasoning skills are of great importance for success in a number of technical fields.For engineering, the ability to examine, manipulate, rotate or twist three-dimensional models in space is of particular interest.Students differ in their development of spatial skills, and this difference affects their performance on some spatial tasks.Instructors involved in teaching the statics and mechanics courses at Texas A&M University at Qatar noticed that some students have challenges in comprehending three-dimensional (3D) technical figures as they presented in engineering textbooks.Consequently, this challenge may lead to limiting in-class instruction and discussion of 3D problems, especially those with vectors representing different physical forces.To help students in understanding these 3D representations and to enhance their spatial skills, we employed the Augmented Reality (AR) technology that enables overlaying 3D figures onto the corresponding 2D figures through a smartphone or a tablet.The students can freely explore the 3D figure from various angles by either moving the book or the device itself.We converted some figures in the textbook used in teaching the Statics subject, which is part of the MEEN 225 Engineering Mechanics course at Texas A&M University.We shared the AR tool in the classroom with students in class and asked them to solve a problem in both 2D (using only book) and 3D (using AR tool).In this paper, we demonstrate the AR tool and share our experience and the assessment results.
Eye Tracking

This chapter provides a basic overview of eye-tracking technology and how the GRID Lab has integrated it with cinematic virtual reality (cine-VR). The GRID Lab has used VR eye tracking to record how effectively hospital residents scan a trauma bay captured with cine-VR. The technical details of that project are discussed in the chapter. Ohio University's GRID Lab and the Better Lab from the University of California San Francisco designed a project using eye-tracking to collect medical training data from a series of cine-VR experiences. The cine-VR experience would run through a custom application built with the Unity game engine. Unity exported the collected data as a standard file-type. All of this data was stored on an air-gapped computer that also controlled the headset.
MIT expert launches Algorithmic Justice League

The battle against bias in facial recognition technology (FRT) has been escalated by a new campaign group called the Algorithmic Justice League, founded by MIT researcher Joy Buolamwini.
Emulating Hierarchical Databases and DHCP with Hipe

The memory bus [14] and courseware, while practical in theory, have not until recently been considered extensive. Given the current status of low-energy communication, systems engineers shockingly desire the investigation of sensor net-works, which embodies the compelling principles of e-voting technology. We construct an algo-rithm for the improvement of cache coherence, which we call Hipe
An extreme bounds analysis of the impact of the COVID-19 outbreak on cryptocurrencies

The aim of this paper is to find out if the COVID-19 outbreak in the USA has a robust impact on the prices of cryptocurrencies. Inspired by the literature related to the determinants of cryptocurrency prices and based on data availability, six potential determinants of cryptocurrency prices and five proxies for the COVID-19 outbreak were selected. The impact of the COVID-19 outbreak was tested using two approaches of extreme bounds analysis and the robustness of our findings was further checked with different cryptocurrencies (Bitcoin, Ethereum, Litecoin and Bitcoin Cash). Our results show that new deaths from the COVID-19 have a robust positive impact on the price of cryptocurrencies while the impact of new confirmed cases, total cases, and total deaths is not robust. In line with previous studies, it is also found that economic uncertainty, stock, gold, and oil prices are robust determinants of the value of cryptocurrencies.
Artificial Inventors

AI is generating patentable inventions without a person involved who qualifies as an inventor. Yet, there are no rules about whether such an invention could be patented, who or what could qualify as an inventor, and who could own the patents. There are laws that require inventors be natural persons, but they predate inventive AI and were never intended to prohibit patents. AI-generated inventions should be patentable because this will incentivize the development of inventive AI and result in more benefits for everyone. When an AI invents, it should be listed as an inventor because listing a person would be unfair to legitimate inventors. Finally, an AI’s owner should own any patents on its output in the same way that people own other types of machine output. The chapter proceeds to address a host of challenges that would result from AI inventorship, ranging from ownership of AI-generated inventions and displacement of human inventors to the need for consumer protection policies.
Implementasi Algoritma Merkle Hellman untuk Keamanan Database

The development of information technology today has a huge impact, namely the issue of security and confidentiality of data. One solution that can be used to guarantee the confidentiality and security of information is cryptography. By using cryptography, a data can be secured through the decryption and encryption process. Security issues and database confidentiality are the most important aspects of an information system. One mechanism to improve database security is to use asymmetric algorithms such as the Merkle Hellmen algorithm. Merkle Hellman is one of the crypto systems that uses the key type of asymmetry. In the Merkle Hellman system, the keys used are 2 different keys, namely the public key and the secret key. Encryption generates ciphertext and decryption produces a plaintext for securing databases that want to be kept confidential. The advantages of this Merkle Hellman algorithm is that there is no need for confidentiality in the key distribution process. From the results of experiments that have been done with this application, the encrypted database becomes a form of message that cannot be understood (ciphertext), but after the decryption process is done, the database is successfully returned to its original form (plaintext) that can be understood
Epigenetic graphitization in the basement of the Siberian craton - evidence of the migration of hydrocarbon-enriched fluids in the paleoproterozoic

For the first time, diagnostics and structural studies of the сarbonaceous material (СM) of quartz-muscovite dinamoschists from schistase zones in biotite migmatites, pegmatites and diabases of the southern part of the Baikal ledge of the Siberian craton were performed. The сarbonaceous material is represented by clearly and microcrystalline graphite with residual hydrocarbon radicals. Native Ni, Sn, zincous Cu, intermetallic compounds of Fe-Ni composition, copper sulfides, rutile, monazite, and zircon were found in intergrowths with СM. The carbon isotopic composition is from -29.19 to -31.58‰, with the exception of CM from schistated diabase with δ13C = -24.93 ‰. The 40Ar/39Ar-dating of muscovite gave an age of 1947 ± 7.8 Ma, indicating a relationship between dynamometamorphism and the accretion of the Akitkan folded system with ancient craton complexes. It is concluded that the deposition of native carbon and metals is caused by the migration of essentially hydrocarbon fluid in the upper crustal formations.
Attention-based argumentation mining

This paper is intended to make a breakthrough in argumentation mining field. Current trends in argumentation mining research use handcrafted features and traditional machine learning (e.g., support vector machine). We worked on two tasks: identifying argument components and recognising insufficiently supported arguments. We utilise deep learning approach and implement attention mechanism on top of it to gain the best result. We do also implement Hierarchical Attention Network (HAN) in this task. HAN is a neural network that gives attention to two levels, which are word-level and sentence-level. Deep learning with attention mechanism models can achieve better result compared with other deep learning methods. This paper also proves that on research task with hierarchically-structured data, HAN will perform remarkably well. We do present our result on using XGBoost instead of a regular non-ensemble classifier as well.
Characteristic Functions and the Central Limit Theorem

The main goal of this chapter is the central limit theorem (CLT) for sums of independent random variables (Theorem 15.38) and for independent arrays of random variables (Lindeberg-Feller theorem, Theorem 15.44). For the latter, we prove only that one of the two implications (Lindeberg’s theorem) that is of interest in the applications. The ideal tools for the treatment of central limit theorems are so-called characteristic functions; that is, Fourier transforms of probability measures. We start with a more general treatment of classes of test functions that are suitable to characterize weak convergence and then study Fourier transforms in greater detail. The subsequent section proves the CLT for real-valued random variables by means of characteristic functions. In the fifth section, we prove a multidimensional version of the CLT.
Learning pathway: Content Management and Identification of Student Behavior Through Online Learning Management in Java

learning path in the online learning system refers to a series of objects which are structured to help students in improving their knowledge in a particular subject. Content is provided to the students and a proper path is set so there will not be any distraction or confusion while studying. In conventional way of teaching if the students would miss one session they will not be able to understand the next session and students will lose interest in the subject, the chances of failure will be more. By providing the content, students can understand the flow so they won’t miss any topic and understand the subject. The session is provided so that the students can be tracked and they wouldn’t miss any topic and score well. Admin will manage the content by adding or removing the content according to the technology and monitor the session time, teachers will get the information of students by admin and teachers will track the students, students will study the content. It is useful to identify the behaviour of the student through the tracking of the user session. Based on the time spent on the content the behavioural pattern of the students is predicted and analysed. The user is analysed based on the behavioural patterns like time spent on the content, skipping of content and the session time. It is hypothesized that the students will be able to get knowledge deeper and can master the subject. It is a web-based java tutoring system which provides an online platform for students. It provides appropriate content for students to learn and understand the concepts clearly.
Earthquakes and Source Theory

This third edition provides a concise yet approachable introduction to seismic theory, designed as a first course for graduate students or advanced undergraduate students. It clearly explains the fundamental concepts, emphasizing intuitive understanding over lengthy derivations, and outlines the different types of seismic waves and how they can be used to resolve Earth structure and understand earthquakes. New material and updates have been added throughout, including ambient noise methods, shear-wave splitting, back-projection, migration and velocity analysis in reflection seismology, earthquake rupture directivity, and fault weakening mechanisms. A wealth of both reworked and new examples, review questions and computer-based exercises in MATLAB®/Python give students the opportunity to apply the techniques they have learned to compute results of interest and to illustrate Earth's seismic properties. More advanced sections, which are not needed to understand the other material, are flagged so that instructors or students pressed for time can skip them.
Measurement Accuracy Assessment for Laser Triangulation 3D Scanning Machine

Laser triangulation 3D scanning machine is one of many types of 3D scanning technologies that are currently available in the current market. It is mainly use to capture object profiles as well as for measurement. Therefore, the measurement accuracy of laser triangulation 3D scanner was assessed and presented in paper. Three solid aluminum calibration block with known dimensions were fabricated by using CNC machine and these samples were named based on its profiles which are round, square and complex. Besides the laser triangulation 3D scanning machine, two more measuring equipment which are Vernier caliper and coordinate measuring machine were used as benchmarks. Three profiles were chose for each calibration block samples that made up of 9 profiles that have been measured and the deviation between the measuring values were analyzed. The results shown that the lowest deviation values for most of the profiles are from coordinate measuring machine and Vernier caliper measurement data. Nevertheless, the measurement deviation for laser triangulation 3D scanner are found to be comparable with other equipment.
Sentiment Analysis Website of Online Hotel Booking Application Reviews Using the Naive Bayes Algorithm

Today, there are many applications available on the Google Play Store, especially the online hotel booking application. In Indonesia, 2 out of 3 people book hotels online and users also rely on digital reviews for travel inspiration as well as research and bookings. Users can find out user satisfaction by looking at reviews from previous users, but it is very problematic if we read the reviews of this application one by one because it takes a very long time. Measuring the level of user satisfaction of an application can be done by knowing how the sentiment from the public. This paper provides an approach to analyzing sentiments for online hotel booking applications based on user reviews on the Google Play Store using the Naive Bayes algorithm. The process starts with data collection using web-scraping, text preprocessing using python, data labeling using SentiStrength, classification with the Naive Bayes algorithm, and website development using Django Web Framework. This website provides information support for users in choosing an online hotel booking application. From this study, the highest accuracy value obtained was 94%.
BITS – A Novel Video Encryption Algorithm

Increased digital information exchange poses a major threat against confidentiality of the information being shared. The information exchange among the parties is said to be efficient only if the transmission process is secure and withstand security breaches. The information shared may be text, image, audio or video. Because of the availability of Internet facility around the world, video became the prime source of information exchange. We all know that anything in the form of visuals will reach the target audience in an efficient manner. They are considered as the major component of the education sector. This paper proposes a novel real time video encryption called BITS. BITS is nothing but Blocking-Inverse-Transposition-Substitution. Initially the video frames are divided into four blocks and the block contents are inverted. Then the entire content of the frame is transpositioned based on the key. And finally the contents of the frame is substituted with different random value. This proposed algorithm is strong against brute-force and statistical attack. The proposed algorithm is suitable for all real time multimedia environments. This algorithm is a compression independent one. The first two phases of the BITS algorithm are implemented using MatLab. The time taken for the computation steps are recorded and analyzed in this paper.