My name is DEBAYAN DEY and I will be your Instructor for the C GHP Course. Now this course is designed for anyone who is interested in learning how an attacker use Google Hacking and get the information of the "Victim" by exploiting various vulnerabilities available. C GHPC is designed by keeping in mind that most of us are not having laptops or computer machine to work for most of the time. That's why in this course curriculum, you need not require any laptop or computer system. Only you need a smartphone and this entire course is 100% practical based!

Are you looking to land a top-paying job in Data Science, AI & Time Series Analysis & Forecasting? Or are you a seasoned AI practitioner who want to take your career to the next level? Or are you an aspiring data scientist who wants to get Hands-on Data Science and Time Series Analysis? If the answer is yes to any of these questions, then this course is for you! This course will teach you the practical skills that would allow you to land a job as a quantitative financial analyst, a data analyst or a data scientist.

Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research proposed machine learning based solutions to auto-label educational content with limited success. In this work, we significantly improve prior research by (1) expanding the input types to include KC descriptions, instructional video titles, and problem descriptions (i.e., three types of prediction task), (2) doubling the granularity of the prediction from 198 to 385 KC labels (i.e., more practical setting but much harder multinomial classification problem), (3) improving the prediction accuracies by 0.5-2.3% using Task-adaptive Pre-trained BERT, outperforming six baselines, and (4) proposing a simple evaluation measure by which we can recover 56-73% of mispredicted KC labels. All codes and data sets in the experiments are available at: https://github.com/tbs17/TAPT-BERT

On-device training for personalized learning is a challenging research problem. Being able to quickly adapt deep prediction models at the edge is necessary to better suit personal user needs. However, adaptation on the edge poses some questions on both the efficiency and sustainability of the learning process and on the ability to work under shifting data distributions. Indeed, naively fine-tuning a prediction model only on the newly available data results in catastrophic forgetting, a sudden erasure of previously acquired knowledge. In this paper, we detail the implementation and deployment of a hybrid continual learning strategy (AR1*) on a native Android application for real-time on-device personalization without forgetting. Our benchmark, based on an extension of the CORe50 dataset, shows the efficiency and effectiveness of our solution.

Abusive language is a massive problem in online social platforms. Existing abusive language detection techniques are particularly ill-suited to comments containing heterogeneous abusive language patterns, i.e., both abusive and non-abusive parts. This is due in part to the lack of datasets that explicitly annotate heterogeneity in abusive language. We tackle this challenge by providing an annotated dataset of abusive language in over 11,000 comments from YouTube. We account for heterogeneity in this dataset by separately annotating both the comment as a whole and the individual sentences that comprise each comment. We then propose an algorithm that uses a supervised attention mechanism to detect and categorize abusive content using multi-task learning. We empirically demonstrate the challenges of using traditional techniques on heterogeneous content and the comparative gains in performance of the proposed approach over state-of-the-art methods.

Global health care expenditure has been projected to grow from US $7.7 trillion in 2017 to US $10 trillion in 2022 at a rate of 5.4% [1]. This translates into health care being an average of 9% of gross domestic product among developed countries [2,3]. Some key global trends that have led to this include tax reform and policy changes in the United States that could impact the expansion of health care access and affordability (Affordable Care Act) [4], implications on the United Kingdom's health care spend based on the decision to leave the European Union [5], population growth and rise in wealth in both China and India [6-8], implementation of socioeconomic policy reform for health care in Russia [9], attempts to make universal health care effective in Argentina [10], massive push for electronic health and telemedicine in Africa [11], and the impact of an unprecedented pace of population aging around the world [12]. From clinicians' perspective there are many important trends that are affecting the way they deliver care of which the growth in medical information is alarming. It took 50 years for medical information to double in 1950. In 1980, it took 7 years. In 2010, it was 3.5 years and is now projected to double in 73 days by 2020 [13].

Geoplotlib - is an open-source Python toolbox that serves to visualize geographical data. It's library supports the development of hardware-accelerated interactive visualizations and provides implementations of dot maps, kernel density estimation, spatial graphs, Voronoi tessellation, shapefiles and many other spatial visualizations.

An Irish student has claimed the second prize and €1,600 in a prestigious international science festival for using artificial intelligence (AI) to forecast the weather. Conor Casey (18), from Pobalscoil Inbhear Scéine, Kenmare, Co Kerry represented Ireland at the Regeneron International Science and Engineering Fair (ISEF), where he claimed second place in his category, earth and environmental sciences. The sixth year student gained the accolade for his project'Using AI to Improve Weather Prediction.' "The whole ISEF experience has been absolutely amazing. It was great that the competition got to go ahead virtually, it gave me the opportunity to interact with people from around the world who have done some incredible work across a huge variety of areas," he said. "I really enjoyed presenting my project and hearing from world famous scientists, who have really encouraged me to develop my love of STEM. "I'm immensely grateful to everyone at SciFest for this opportunity, as well as all my family and my teacher Ms Sarah Abbott, without whom none of this would have been possible." Using artificial intelligence, Conor developed a forecast model with a similar level of accuracy to current models but with "greater efficiency." Conor's goal was to make it cheaper to generate weather forecasts by reducing the amount of resources required. The young scientist secured his place at the international competition when he won the runner-up award at the SciFest 2020 National Final last November. The event is the largest, most inclusive STEM fair programme for second level students in Ireland. Get today's news headlines, opinion, sport and more direct to your inbox at 7.30am every morning, and every evening, with our free daily newsletter. Regeneron ISEF is the world's largest international pre-college science competition, involving over 1,800 students from 64 countries, regions and territories competing for a prize fund of over €4million. The competition is usually held in the United States but for the second year running this year's event took place virtually. Sheila Porter, SciFest Founder and CEO, said: "We are all very proud of Conor, he is a great example of the high calibre of entries that SciFest produces.

More fourth-grade Japanese students play video games or mobile games on school days than children of the same age did nine years ago, a health ministry survey has shown. The rate came to 74.8% for children born in 2010, up from 65.2% in a similar survey conducted on children born in 2001 when they reached the age of 10. The health ministry conducted the latest survey last year on some 26,000 children born in 2010 and collected answers from 92% of them. Of the respondents, those who play games for less than an hour on school days accounted for 71.2% when they were first-grade students. But the proportion fell to 48.1% when they entered the fourth grade.

Organizations are discovering how artificial intelligence and machine learning can transform their business. AI's contribution to global GDP is expected to grow from $2 trillion in 2019 to $15 trillion in 2030 according to PwC. Every organization needs professionals to digest data and translate it into action, but the labor market is woefully unprepared to meet the exponential growth in demand. How do we start the AI revolution without any revolutionaries? Sometimes the answer lies within.