This document discusses various text mining and natural language processing techniques. It begins with an overview of text mining and its importance for analyzing unstructured data sources. It then demonstrates bag-of-words modeling and discusses preprocessing text, such as stemming. The document shows how to generate term frequency-inverse document frequency (TF-IDF) matrices and create word clouds to analyze corpora. It provides an example of using these techniques to analyze customer reviews of Xbox. Finally, it discusses using techniques like latent Dirichlet allocation, lexicons, and emotion mining to analyze clinical trial data and extract structured information, sentiments, and emotions.
Framework for developing algorithmic fairnessjournalBEEI
In a world where the algorithm can control the lives of society, it is not surprising that specific complications in determining the fairness in the algorithmic decision will arise at some point. Machine learning has been the de facto tool to forecast a problem that humans cannot reliably predict without injecting some amount of subjectivity in it (i.e., eliminating the “irrational” nature of humans). In this paper, we proposed a framework for defining a fair algorithm metric by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike). The researcher can then adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness. Therefore, future work for this domain would have a more straightforward development process. We also found while structuring this framework that to develop a concept of fairness that everyone can accept, it would require collaboration with other domain expertise (e.g., social science, law, etc.) to avoid any misinformation or naivety that might occur from that particular subject. That is because this field of algorithmic fairness is far broader than one would think initially; various problems from the multiple points of view could come by unnoticed to the novice’s eye. In the real world, using active discriminator attributes such as religion, race, nation, tribe, religion, and gender become the problems, but in the algorithm, it becomes the fairness reason.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
Why is image analytics Important? What good can come of caption generation or image descriptions? And how does Data Science & Machine learning techniques work on Image Analytics and to what purpose? We see how it works for the retail industry and for the Healthcare industry. What more? Take a look...
Sentiment analysis is an important current research area. The demand for sentiment analysis and classification is growing day by day; this paper presents a novel method to classify Urdu documents as previously no work recorded on sentiment classification for Urdu text. We consider the problem by determining whether the review or sentence is positive, negative or neutral. For the purpose we use two machine learning methods Naïve Bayes and Support Vector Machines (SVM) . Firstly the documents are preprocessed and the sentiments features are extracted, then the polarity has been calculated, judged and classify through Machine learning methods.
Sentiment Analysis Using Hybrid Approach: A SurveyIJERA Editor
Sentiment analysis is the process of identifying people’s attitude and emotional state’s from language. The main objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Opinion mining, also known as Sentiment analysis, plays an important role in this process. It is the study of emotions i.e. Sentiments, Expressions that are stated in natural language. Natural language techniques are applied to extract emotions from unstructured data. There are several techniques which can be used to analysis such type of data. Here, we are categorizing these techniques broadly as ”supervised learning”, ”unsupervised learning” and ”hybrid techniques”. The objective of this paper is to provide the overview of Sentiment Analysis, their challenges and a comparative analysis of it’s techniques in the field of Natural Language Processing.
Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.
Framework for developing algorithmic fairnessjournalBEEI
In a world where the algorithm can control the lives of society, it is not surprising that specific complications in determining the fairness in the algorithmic decision will arise at some point. Machine learning has been the de facto tool to forecast a problem that humans cannot reliably predict without injecting some amount of subjectivity in it (i.e., eliminating the “irrational” nature of humans). In this paper, we proposed a framework for defining a fair algorithm metric by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike). The researcher can then adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness. Therefore, future work for this domain would have a more straightforward development process. We also found while structuring this framework that to develop a concept of fairness that everyone can accept, it would require collaboration with other domain expertise (e.g., social science, law, etc.) to avoid any misinformation or naivety that might occur from that particular subject. That is because this field of algorithmic fairness is far broader than one would think initially; various problems from the multiple points of view could come by unnoticed to the novice’s eye. In the real world, using active discriminator attributes such as religion, race, nation, tribe, religion, and gender become the problems, but in the algorithm, it becomes the fairness reason.
COMPREHENSIVE ANALYSIS OF NATURAL LANGUAGE PROCESSING TECHNIQUEJournal For Research
Natural Language Processing (NLP) techniques are one of the most used techniques in the field of computer applications. It has become one of the vast and advanced techniques. Language is the means of communication or interaction among humans and in present scenario when everything is dependent on machine or everything is computerized, communication between computer and human has become a necessity. To fulfill this necessity NLP has been emerged as the means of interaction which narrows the gap between machines (computers) and humans. It was evolved from the study of linguistics which was passed through the Turing test to check the similarity between data but it was limited to small set of data. Later on various algorithms were developed along with the concept of AI (Artificial Intelligence) for the successful execution of NLP. In this paper, the main emphasis is on the different techniques of NLP which have been developed till now, their applications and the comparison of all those techniques on different parameters.
Why is image analytics Important? What good can come of caption generation or image descriptions? And how does Data Science & Machine learning techniques work on Image Analytics and to what purpose? We see how it works for the retail industry and for the Healthcare industry. What more? Take a look...
Sentiment analysis is an important current research area. The demand for sentiment analysis and classification is growing day by day; this paper presents a novel method to classify Urdu documents as previously no work recorded on sentiment classification for Urdu text. We consider the problem by determining whether the review or sentence is positive, negative or neutral. For the purpose we use two machine learning methods Naïve Bayes and Support Vector Machines (SVM) . Firstly the documents are preprocessed and the sentiments features are extracted, then the polarity has been calculated, judged and classify through Machine learning methods.
Sentiment Analysis Using Hybrid Approach: A SurveyIJERA Editor
Sentiment analysis is the process of identifying people’s attitude and emotional state’s from language. The main objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Opinion mining, also known as Sentiment analysis, plays an important role in this process. It is the study of emotions i.e. Sentiments, Expressions that are stated in natural language. Natural language techniques are applied to extract emotions from unstructured data. There are several techniques which can be used to analysis such type of data. Here, we are categorizing these techniques broadly as ”supervised learning”, ”unsupervised learning” and ”hybrid techniques”. The objective of this paper is to provide the overview of Sentiment Analysis, their challenges and a comparative analysis of it’s techniques in the field of Natural Language Processing.
Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.
Bharath Sudharsan, ArmadaHealth - NLP in Aid of Critical Health Decisions - H...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/LwcQo2gxxog
Bio: Bharath Sudharsan is the Director of Data Science and Innovation at ArmadaHealth. He leads a team of data analysts who develop and implement AI tools that are at the heart of objective and data-driven specialty care referral process synonymous with ArmadaHealth. Bharath has also held positions at Fractal Analytics and Quanttus, Inc. and WellDoc, Inc. He is also the founder of Geetha, LLC, a provider of best in class healthcare analytics consultation including implementation of NLP and AI.
Keynote by Charles Elkan, Goldman Sachs - Machine Learning in Finance - The P...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/7h61dxKjhvg
Machine learning in finance—the promise and the peril
This talk will discuss how machine learning (ML) fits into the landscape of quantitative methods used in finance, and draw conclusions about application domains where ML is more promising versus domains where the perils are more acute. The talk will also discuss how to formulate a financial goal as an ML problem, and how to choose between solution approaches.
Bio: Leading projects to apply machine learning and artificial intelligence across the firm. Evaluating opportunities to work with other organizations and consulting with clients.
Demonstration of a significant bias of some French court of appeal judges in decisions about the rights of asylum.
www.supralegem.fr
Presentation meetup ML Paris Feb 17th, 2016
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Intelligent information extraction based on artificial neural networkijfcstjournal
Question Answering System (QAS) is used for information retrieval and natural language processing
(NLP) to reduce human effort. There are numerous QAS based on the user documents present today, but
they all are limited to providing objective answers and process simple questions only. Complex questions
cannot be answered by the existing QAS, as they require interpretation of the current and old data as well
as the question asked by the user. The above limitations can be overcome by using deep cases and neural
network. Hence we propose a modified QAS in which we create a deep artificial neural network with
associative memory from text documents. The modified QAS processes the contents of the text document
provided to it and find the answer to even complex questions in the documents.
Laboratorio Master BI&BDA (Modulo Web Data Analytics) : Reddit fashion insightsCarla Marini
Laoratorio svolto al Master in Business Intelligence & Big Dat Analytic, nel modulo Web Data Analytics
Analisi degli argomenti che trattano temi relativi alla moda in Reddit. Data Scraping, Data Cleaning, Data Clustering, Text Mining and Sentiment Analysis.
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
Three experiments I have done with data science. Related to text analysis, integration. Focusing on the learning's rather than details on how it was done with source code. I feel it is important to see this subject in relation to business problems rather than as pure branch of Statistics. Focusing on what has to be done enabled me to find the right solution from a complicated and very interesting subject.
Workshop given to the staff for PhD and Masters Topic Selection in the area of Big Data, Data Science and Machine Learning. It has many interactive online demos to understanding on NLP social media analysis like sentiment analysis , topic modeling , language detection and intent detection. Some of the basic concept about classification and regression and clustering with interactive worksheets. Finally , hands-on machine learning models and comparisons in WEKA tool kit with case study of cars and diabetic patient data.
Data Analysis in Research: Descriptive Statistics & NormalityIkbal Ahmed
A Presentation on Data Analysis using descriptive statistics & normality. From this presentation you can know-
1) What is Data
2) Types of Data
3) What is Data analysis
4) Descriptive Statistics
5) Tools for assessing normality
Bharath Sudharsan, ArmadaHealth - NLP in Aid of Critical Health Decisions - H...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/LwcQo2gxxog
Bio: Bharath Sudharsan is the Director of Data Science and Innovation at ArmadaHealth. He leads a team of data analysts who develop and implement AI tools that are at the heart of objective and data-driven specialty care referral process synonymous with ArmadaHealth. Bharath has also held positions at Fractal Analytics and Quanttus, Inc. and WellDoc, Inc. He is also the founder of Geetha, LLC, a provider of best in class healthcare analytics consultation including implementation of NLP and AI.
Keynote by Charles Elkan, Goldman Sachs - Machine Learning in Finance - The P...Sri Ambati
This session was recorded in NYC on October 22nd, 2019 and can be viewed here: https://youtu.be/7h61dxKjhvg
Machine learning in finance—the promise and the peril
This talk will discuss how machine learning (ML) fits into the landscape of quantitative methods used in finance, and draw conclusions about application domains where ML is more promising versus domains where the perils are more acute. The talk will also discuss how to formulate a financial goal as an ML problem, and how to choose between solution approaches.
Bio: Leading projects to apply machine learning and artificial intelligence across the firm. Evaluating opportunities to work with other organizations and consulting with clients.
Demonstration of a significant bias of some French court of appeal judges in decisions about the rights of asylum.
www.supralegem.fr
Presentation meetup ML Paris Feb 17th, 2016
A Framework for Arabic Concept-Level Sentiment Analysis using SenticNet IJECEIAES
Arabic Sentiment analysis research field has been progressing in a slow pace compared to English and other languages. In addition to that most of the contributions are based on using supervised machine learning algorithms while comparing the performance of different classifiers with different selected stylistic and syntactic features. In this paper, we presented a novel framework for using the Concept-level sentiment analysis approach which classifies text based on their semantics rather than syntactic features. Moreover, we provided a lexicon dataset of around 69 k unique concepts that covers multi-domain reviews collected from the internet. We also tested the lexicon on a test sample from the dataset it was collected from and obtained an accuracy of 70%. The lexicon has been made publicly available for scientific purposes.
Random Forest Tutorial | Random Forest in R | Machine Learning | Data Science...Edureka!
This Edureka Random Forest tutorial will help you understand all the basics of Random Forest machine learning algorithm. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn random forest analysis along with examples. Below are the topics covered in this tutorial:
1) Introduction to Classification
2) Why Random Forest?
3) What is Random Forest?
4) Random Forest Use Cases
5) How Random Forest Works?
6) Demo in R: Diabetes Prevention Use Case
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Intelligent information extraction based on artificial neural networkijfcstjournal
Question Answering System (QAS) is used for information retrieval and natural language processing
(NLP) to reduce human effort. There are numerous QAS based on the user documents present today, but
they all are limited to providing objective answers and process simple questions only. Complex questions
cannot be answered by the existing QAS, as they require interpretation of the current and old data as well
as the question asked by the user. The above limitations can be overcome by using deep cases and neural
network. Hence we propose a modified QAS in which we create a deep artificial neural network with
associative memory from text documents. The modified QAS processes the contents of the text document
provided to it and find the answer to even complex questions in the documents.
Laboratorio Master BI&BDA (Modulo Web Data Analytics) : Reddit fashion insightsCarla Marini
Laoratorio svolto al Master in Business Intelligence & Big Dat Analytic, nel modulo Web Data Analytics
Analisi degli argomenti che trattano temi relativi alla moda in Reddit. Data Scraping, Data Cleaning, Data Clustering, Text Mining and Sentiment Analysis.
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
Three experiments I have done with data science. Related to text analysis, integration. Focusing on the learning's rather than details on how it was done with source code. I feel it is important to see this subject in relation to business problems rather than as pure branch of Statistics. Focusing on what has to be done enabled me to find the right solution from a complicated and very interesting subject.
Workshop given to the staff for PhD and Masters Topic Selection in the area of Big Data, Data Science and Machine Learning. It has many interactive online demos to understanding on NLP social media analysis like sentiment analysis , topic modeling , language detection and intent detection. Some of the basic concept about classification and regression and clustering with interactive worksheets. Finally , hands-on machine learning models and comparisons in WEKA tool kit with case study of cars and diabetic patient data.
Data Analysis in Research: Descriptive Statistics & NormalityIkbal Ahmed
A Presentation on Data Analysis using descriptive statistics & normality. From this presentation you can know-
1) What is Data
2) Types of Data
3) What is Data analysis
4) Descriptive Statistics
5) Tools for assessing normality
Understanding Users Through Ethnography and Modeling - STC Summit 2010Jim Jarrett
90 minute training for experienced practitioners in best practices for analyzing and modeling qualitative user research, including KJ Analysis, personas, and scenarios. Tips and tricks and techniques included. Presented at the STC Summit 2010 on 3 May 2010.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
ExcelR has trainers who have over 15 years of experience on an average, in Agile methodology process implementation, managing, playing role of Agile coach etc. This will ensure that you get the best from the best.
ExcelR has trainers who have over 15 years of experience on an average, in Agile methodology process implementation, managing, playing role of Agile coach etc. This will ensure that you get the best from the best.
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers training on PMI Agile Certification which clearly explains the Agile methodologies and techniques for managing successful project completion
ExcelR offers training on PMI Agile Certification which clearly explains the Agile methodologies and techniques for managing successful project completion
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.