Reviewerly/exHarmony huggingface dataset:
Optimal design of CMOS current mode instrumentation amplifier using bio-inspired method for biomedical applications

Analog integrated circuits for biomedical applications require good performance. This paper presents an instrumentation amplifier (IA) design based on three complementary metal oxide semiconductor (CMOS) conveyors with an active resistor. This circuit offers the possibility to control the gain by voltage and current. We have designed the IA to minimize the parasitic resistance (Rx) with large bandwidth and high common mode rejection ratio (CMRR) using the artificial bee colony algorithm (ABC). The topology is simulated using 0.35µm CMOS technology parameters. The optimization problem is represented by an objective function that will be implemented using MATLAB script. The results were approved by the simulation using the advanced design system (ADS) tool. The simulation results were compared to the characteristics of some other instrumentation amplifiers exsisting in the literature. The circuit has a higher CMRR than other topologies.
The Use of Negative Politeness Strategies in Casual Conversation Among the Graduate Program Students

A conversation is considered as the center of human interaction. People used politeness strategies to minimize and avoid conflict that may occur in conversation. The phenomena showed that the graduate students spontaneously did their conversation with their friends who have different cultures did not realize that they used negative politeness strategies. This study aims to explain the use of negative politeness strategies in casual conversation among the English Education Department's graduate program students. The participants of this study were fourteen students of the graduate program of the English Education Department. Descriptive qualitative research with interpretative data analysis was employed in the present study. The results showed that the students used negative politeness strategy, which consists of; be conventionally indirect, question and hedge, be pessimistic, minimize the imposition, apologize, impersonalize the speaker and the hearer, state the FTA as a general rule, and go on record as incurring debt or as not indebting the hearer. It can be concluded that the study found eight sub-strategies out of ten sub-strategies of negative politeness. It has benefits for the students to improve their knowledge, especially in using negative politeness strategies.
Smart Health Monitoring and Management Using Internet of Things, Artificial Intelligence with Cloud Based Processing

: Smart health monitoring system is a system that shortens the distance between a patient and the relevant medical organization. These systems have rapidly evolved during the past two decades and have the potential to change the way health cases are currently delivered. The Internet of Things (IoT) is an innovation for smart health management. It provides monitoring patients remotely and guarantees giving patients the medication and getting complete health care without the latter getting infected. As we know that the NovelCorona-virus also known as covid-19 expanded its impacts from China and still expands its catchment, national as well as international measures are being taken to contain the outbreak such as the placing of lockdown in nations. As a result, many people are being infected making the hospital incapable of providing proper healthcare. This paper proposes a smart health system that monitors the patients holding the coronavirus remotely and to protect the lives of the health service members (like physicians, nurses) from infection. This smart system observes patients by using sensors, to gather rich information every minute seconds. This benefits the patient as well as the service members because the physicians can observe the patient while freeing up beds in the hospitals for the critical cases.
Debugging Classical Ontologies Using Defeasible Reasoning Tools

A successful application of ontologies relies on representing as much accurate and relevant domain knowledge as possible, while maintaining logical consistency. As the successful implementation of a real-world ontology is likely to contain many concepts and intricate relationships between the concepts, it is necessary to follow a methodology for debugging and refining the ontology. Many ontology debugging approaches have been developed to help the knowledge engineer pinpoint the cause of logical inconsistencies and rectify them in a strategic way. We show that existing debugging approaches can lead to unintuitive results, which may lead the knowledge engineer to opt for deleting potentially crucial and nuanced knowledge. We provide a methodological and design foundation for weakening faulty axioms in a strategic way using defeasible reasoning tools. Our methodology draws from Rodler’s interactive ontology debugging approach and extends this approach by creating a methodology to systematically find conflict resolution recommendations. Importantly, our goal is not to convert a classical ontology to a defeasible ontology. Rather, we use the definition of exceptionality of a concept, which is central to the semantics of defeasible description logics, and the associated algorithm to determine the extent of a concept’s exceptionality (their ranking); then, starting with the statements containing the most general concepts (the least exceptional concepts) weakened versions of the original statements are constructed; this is done until all inconsistencies have been resolved.
Detecting potential money laundering addresses in the Bitcoin blockchain using unsupervised machine learning

Money laundering is a serious problem worldwide, especially in the crypto market.This is mostly because of the anonymity that many cryptocurrencies offer.That is one of the reasons why cryptocurrencies are a haven for money laundering, because it is easier for criminal entities to buy the currency and then trade it for real fiat money.Detecting money laundering in cryptocurrency can be tricky because the crypto network is large and convoluted and nearly impossible to analyze by hand.What we can do is look at addresses that took part in transactions as actors and then use machine learning to predict what addresses are possibly laundering money.In this paper we intend to analyze methods that can be used to detect money laundering in Bitcoin using machine learning to empower investigators to more accurately and efficiently determine whether a suspicious activity is money laundering.1 Lucinity is an AML software company that uses advanced AI systems to discover money laundering.
Producing Generative Digital Data Objects: An Empirical Study on COVID-19 Data Flows in Online Communities

Digital data objects on viruses have played a pivotal role in the fight against COVID-19, leading to healthcare innovation such as new diagnostics, vaccines, and societal intervention strategies.To effectively achieve this, scientists access viral data from online communities (OCs).The social-interactionist view on generativity, however, has put little emphasis on data.We argue that generativity on data depends on the number of data instances, data timeliness, and completeness of data classes.We integrated and analyzed eight OCs containing SARS-CoV-2 nucleotide sequences to explore how community structures influence generativity, revealing considerable differences between OCs.By assessing provided data classes from user perspectives, we found that generativity was limited in two important ways: When required data classes were either insufficiently collected or not made available by OC providers.Our findings highlight that OC providers control generativity of data objects and provide guidance for scientists selecting OCs for their research.How do scientific online communities shape digital data objects on SARS-CoV-2 and COVID-19?Our findings shed light on the structures of OCs and the potential of viral data to produce innovation, which is reflected in the concept of generativity.We also provide useful insights for the current and future viral outbreaks.The paper initially reviews the social-interactionist view on generativity in OCs.Thereupon we describe our explorative methodological approach, which covers data collection, integration, in-depth analysis, and visualization.Afterward, we discuss and reflect on our findings on OC structures and digital data objects.
Design and implementation of children’s gesture education games based on AI gesture recognition technology

In order to cultivate children’s imagination and creativity in the cognitive process, combined with the traditional hand shadow game, a children’s gesture education game based on AI gesture recognition technology is designed and developed. The game uses unity development platform, with children’s digital gesture recognition as the content, designs and implements the basic functions involved in the game, including AI gesture recognition function, character animation function, interface interaction function, AR photo taking function and question answering system function. The game is finally released on the mobile terminal. Players can recognize gestures through mobile cameras, interact with virtual cartoon characters in the game, watch cartoon character animation, understand popular science knowledge, and complete the answers in the game. The educational games can better assist children to learn digital gestures, enrich children’s ways of cognition, expand children’s imagination, and let children learn easily with happy educational games.
Text Document Clustering using K-Means and Dbscan by using Machine Learning

With the growth of today’s world, text data is also increasing which are created by different media like social networking sites, web, and other informatics and sources e.t.c . Clustering is an important part of the data mining. Clustering is the procedure of cleave the large &amp;similar type of text into the same group. Clustering is generally used in many applications like medical, biology, signal processing, etc. Algorithm contains traditional clustering like hierarchal clustering, density based clustering and self-organized map clustering. By using k-means features and dbscan we can able to cluster the document. dbscan a part of clustering shows to a number of standard. The data sets will automatically evaluate the formulation of each and every part data through by the use of dbscan and k-means that will shows the clustering power of the data. document consists of multiple topic. Document clustering demands the context of signifier and form ancestry. Descriptors are the expression used to describe the satisfied inside the cluster.
A Pseudorandom Sequence Generated over a Finite Field Using The Möbius Function

The aim of this paper is to generate and examine a pseudorandom sequence over a finite field using the Möbius function. In the main part of the paper, after generating a number of sequences using the Möbius function, we examine the sequences’ pseudorandomness using autocorrelation and prove that the second half of any sequence in $\mathbb{F}_{3^n}$ is the same as the first, but for the sign of the terms. I reach the conclusion, that it is preferable to generate sequences in fields of the form $\mathbb{F}_{3^n}$, thereby obtaining a sequence of the numbers $-1$,$0$,$1$, each of which appear in the same amounts. There is a variety of applications of the discussed pseudorandom generator and other generators such as cryptography or randomized algorithms.
Big Book in Early Reading Learning in Lower-Class Elementary School

Problems at the initial reading stage must be overcome so that students do not have difficulty in reading. Efforts are made to overcome the problem of reading at the beginning of grade I elementary school students by using big book learning media. This research aims to know: (1) the use of the big book in reading learning begins. (2) student learning outcomes by using big books in initial reading activities in Karangpatihan 3 Elementary School. This research is qualitative research with a type of descriptive research. The study subjects were students of class I SDN 3 Karangpatihan, totaling 23 people. Data collection techniques in the form of tests and observations. The results showed an average score at the pre-tests of 75.43 and post-tests 80.65. Post-test results showed 13 students in the good category at 56.52% and 10 students in the good category with a percentage of 43.48%. Thus, it can be concluded that the use of Big Book media positively influences the ability to read the beginning in 1st-grade students in Karangpatihan 3 Elementary School.
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection

The paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus.Previous works have tried to mitigate this problem with additional pre-training, either on the downstream corpus itself when it is large enough, or on a manually curated unlabeled corpus of a similar domain.In this paper, we address the problem for the case when the downstream corpus is too small for additional pre-training.We propose TADPOLE, a task adapted pre-training framework based on data selection techniques adapted from Domain Adaptation.We formulate the data selection as an anomaly detection problem that unlike existing methods works well when the downstream corpus is limited in size.It results in a scalable and efficient unsupervised technique that eliminates the need for any manual data curation.We evaluate our framework on eight tasks across four different domains: Biomedical, Computer Science, News, and Movie reviews, and compare its performance against competitive baseline techniques from the area of Domain Adaptation.Our framework outperforms all the baseline methods.On small datasets with less than 5K training examples, we get a gain of 1.82% in performance with additional pre-training for only 5% steps.It also compliments some of the other techniques such as data augmentation known for boosting performance when downstream corpus is small; highest performance is achieved when data augmentation is combined with task adapted pre-training.
Requirements for the Training of Masters of Natural Sciences of Pedagogical Higher Educational Institutions

The article deals with the issues of training future teachers of natural sciences; findings: what requirements for professional education are typical for the training of a teacher of a new formation; what role in the professional development of specialists of pedagogical HEI has professional training and what shortcomings in the training of masters of natural sciences occur at the present stage of development of higher education; areas of modernization of vocational education and physical education, in particular, are identified by scientists.
Sinematografi Pelatihan Pembuatan Film Bersama Teater Matan dan Mahasiswa FIB Unilak

In this modern era marked by the discovery of advanced technology, it requires a creative young generation, so that they can compete in accordance with the demands of the times. One of the areas of creativity that gets that place is creativity in creating films. Films can be used as a place to express feelings, as well as be used as a product promotion or area where the film's work is produced. This fact is strengthened by the increasing inclusion of social media such as YouTube, Facebook, Twitter, and Instagram which provide video media in their content. To produce good film work requires a process of putting into practice the theories of filmmaking. With this film-making training, at least the members of the Matan Theater can get to know more closely what film is and can practice making good and correct films at the same time.
Prospects and challenges in the investigation of credit card fraud in Vall Region of the Gauteng Province, South Africa

Today use of Credit Card even in developing countries has become a common scenario. People use it to shop, pay bills and for online transactions. But with increase in number of Credit Card users, the cases of fraud in Credit Card have also been on rise. Credit Card related frauds cause globally a loss of billions of Rands. Credit Card fraud can be done in numerous ways. The article begins with an examination of the extent of the challenge and response by the relevant stakeholders, especially the Criminal Justice System (CJS). This study was carried out utilising a qualitative research approach with a convenience, purposive and snowball sampling techniques. Thirtynine (39) interviews were conducted to solicit the views of the participants and police investigators from Vanderbijlpark, Sebokeng, Sharpeville and Vereeniging police stations, members of the community, and victims of credit card fraud were interviewed. These interviews were analysed according to the phenomenological approach, aided with the inductive Thematic Content Analysis (TCA) to identify the participants’ responses and themes.&#x0D; The findings indicated that the extent of credit card fraud in Vaal region is reaching alarming rates. Based on the findings, the authors provided recommendations such as: police investigators being taken for regular workshops and training on how to investigate sophisticated methods used by perpetrators such as technology, awareness in the society about credit card fraud should be prioritised and enhanced. This study recommends that the CCTV cameras should be installed in the ATM, where cases of credit card are taking place. In addition, the police be visible in the areas which are most prevalent to credit card fraud.
Analysis of Heterogeneous Device Characteristics in Round Robin Based Load Balancing Algorithm with Closest Data Center as Service Broker Policy in Cloud

Load balancing is an important aspect in cloud to share load among different virtual machines running on various physical nodes. The user response time which is an important performance metric is being highly influenced by the efficient load balancing algorithm for cloud data centers. Virtual machines which are part of the cloud data centers consist of various types of physical devices. The user response time is affected significantly by the capacity of physical devices that exist as part of the data centers. Several load balancing algorithms exist in the literature to allocate task effectively on various virtual machines running in data centers. We investigate the performance of round robin based load balancing algorithm with closest data center as service broker policy in cloud data centers. We have performed a simulation with data centers that consist of devices with different physical characteristics such as memory, storage, bandwidth, processor speed and scheduling policy using Round Robin load balancing algorithm with closest data centers as service broker policy. We present the merits of heterogeneous device characteristics in reducing the user response time and the data center request service time. We used Cloud Analyst, an open source simulation tool for cloud computing environment.
Geology and pyrite-polymetallic mineralization of the forecasted&#x0D; Limonite deposit, Rassokhinsky ore cluster (Yenisei ridge)

The development of Russia’s largest Gorevskoye leadzinc&#x0D; deposit (Gorevsky GOK, Novoangarsky OK) will&#x0D; depend on promising deposits of the Angara polymetallic&#x0D; ore region, in particular, those of the Rassokhinsky&#x0D; ore cluster. Based on the analysis of geological and geophysical&#x0D; information of previous and current studies,&#x0D; a major Limonite pyrite-polymetallic (massive sulfide)&#x0D; deposit is forecasted within it. The main lithological&#x0D; and stratigraphic criterion for the localization of ores is&#x0D; determined by their confinement to high-carbon black&#x0D; quartz-sericite schists of the upper subformation of the&#x0D; Potoskuy suite of the Upper Riphean. In the top of this&#x0D; pack, layered and massive pyrite-polymetallic ores, 70–&#x0D; 140 m thick, are localized. Based on PPA data, lead and&#x0D; zinc content in core varies from fractions of a percent&#x0D; to several percent, based on the ICP-AES method: 0,1–&#x0D; 3,7 % Pb, 0,1–6,4 % Zn, Pb : Zn ratio is 1 : (1–10).
Monitoring Elementary School Students Achievement using Android-based RESTful Web Service

There are still many elementary schools that have not implemented an information system for processing student grades online because of the assumption that this is still not needed, even though in fact the use of computerized information systems can help schools in improving the academic process to be more effective and efficient. Information systems with web-based applications that are now often found still have shortcomings, namely the mobility of their use is still lacking compared to Android-based applications which are now easier to use and access anywhere. The use of web applications as a means for teachers to input values as well as an android application as a means for parents to be able to monitor their children's academic scores is considered to be the most appropriate solution in implementing this information system, but differences in platforms between Android and the web make it difficult for data to be integrated with each other. Therefore we need an interoperability system in order to integrate applications from different platforms. Then an android based academic score monitoring application was made with RESTful web service. The selection of the RESTful web service system itself is because this system applies the concept of a client server where the server to be created is a web-based application that is used by the school to input student grades, and the client application is an android application intended for parents / guardians of students to get information regarding the value of his child. The output of this research is an application system for monitoring the academic scores of elementary school students by implementing the RESTful web service system in its application as well as the results of the application testing questionnaire based on the usability aspect of the application with the final result of the application eligibility percentage of 87%.Keywords: Monitoring Student Values, Platforms, Information Systems.