The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.

This research introduces an innovative mathematical learning approach that integrates generative AI to cultivate a structured learning rather than quick solution. Our method combines chatbot capabilities and generative AI to offer interactive problem-solving exercises, enhancing learning through a stepby-step approach for varied problems, advocating for the responsible use of AI in education. Our approach emphasizes that immediate answers from ChatGPT can impede real learning. We introduce a reward-based system that requires students to solve mathematical problems effectively to receive the final answer. This encourages a progressive learning path from basic to complex problems, rewarding mastery with final solutions. The goal is to transition students from seeking quick fixes to engaging actively in a comprehensive learning experience.

A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls. Currently, when the orbit lock system fails operators must manually intervene. This paper develops a Machine Learning based fault detection methodology to identify orbit lock anomalies and notify accelerator operations staff of the off-normal behavior. Our method is unsupervised, so it does not require labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to capture normal patterns and predict future values of monitoring sensors in the orbit lock system. Anomalies are detected when the prediction error exceeds a threshold. We conducted experiments using monitoring data from Jefferson Lab's Continuous Electron Beam Accelerator Facility (CEBAF). The results are promising: the percentage of real anomalies identified by our solution is 68.6%-89.3% using monitoring data of a single component in the orbit lock control system. The accuracy can be as high as 82%.

Reciprocal questioning is essential for effective teaching and learning, fostering active engagement and deeper understanding through collaborative interactions, especially in large classrooms. Can large language model (LLM), such as OpenAI's GPT (Generative Pre-trained Transformer) series, assist in this? This paper investigates a pedagogical approach of classroom flipping based on flipped interaction in LLMs. Flipped interaction involves using language models to prioritize generating questions instead of answers to prompts. We demonstrate how traditional classroom flipping techniques, including Peer Instruction and Just-in-Time Teaching (JiTT), can be enhanced through flipped interaction techniques, creating student-centric questions for hybrid teaching. In particular, we propose a workflow to integrate prompt engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a quiz-prompt-discuss routine to empower students to self-regulate their learning capacity and enable teachers to swiftly personalize training pathways. We develop an LLM-driven chatbot software that digitizes various elements of classroom flipping and facilitates the assessment of students using these routines to deliver peer-generated questions. We have applied our LLM-driven chatbot software for teaching both undergraduate and graduate students from 2020 to 2022, effectively useful for bridging the gap between teachers and students in remote teaching during the COVID-19 pandemic years. In particular, LLM-driven classroom flipping can be particularly beneficial in large class settings to optimize teaching pace and enable engaging classroom experiences.

Detailed JD: • Minimum of 15 years of technical experience in Oracle ERP (Oracle Cloud/PeopleSoft) • Experience with preparation of data strategy, migration plan, object dependencies, etc. • Experience in Oracle Financials Cloud Schema and Data model • Experience with Master (customer, supplier, COA, etc.) and transaction (GL, PO, AP, etc.) data in Oracle Financials Cloud • Experience with conducting impact assessment on outbound data payload from Oracle Financials Cloud to data lake • Experience in creating design document for accommodating changes to the payload • Experience with optimizing data transfer (Extraction, Cleansing,Transformation, Loading and Validation) from Oracle Financials Cloud to data lake • Hands on with writing complex SQL • Excellent oral and written communication skills • Good understanding of PeopleSoft financial data model • Experience in data lake architecture Apply Here For Remote Business Architect/ Portland, OR ( Remote),6-12 months contract roles, visit Remote Business Architect/ Portland, OR ( Remote),6-12 months contract Roles

STERLING, Va., Dec. 5, 2019 /PRNewswire-PRWeb/ -- EnterWorks, a leading provider of Master Data Management (MDM) and Product Information Management (PIM) solutions, has announced a live webcast event featuring Michele Goetz, Principal Analyst, Business Insights, Information Architecture and Artificial Intelligence, at Forrester. The webinar, "How AI, Machine Learning and Data Strategy Can Enable Compelling New Products & Experiences," will take place on Tuesday, December 10, 2019 from 11:00 am to 12:00 pm EST. It is sponsored by EnterWorks; Amplifi, an information management consultancy that helps the world's leading brands, retailers and manufacturers to harness and unleash the power of their data; and Sisense, a business intelligence software and analytics platform. The webinar will inform participants how artificial intelligence, machine learning and data strategy can enable compelling new products and experiences, and how deploying AI and ML depends on effective master data and its proper governance. According to Forrester's Goetz, many companies have initiated AI and ML projects only to find that they have not established the foundation for success that comes with implementing a comprehensive data management strategy and the platforms needed to make replicable and scalable success possible.

A group of veterans inspired by the need to keep schools and public spaces safer have created a new technology they say can detect guns and send out alerts before shots are ever fired. Active shooter situations have played out across the country – a gunman opened fire inside a Florida high school, shots rang out at a Texas Walmart and multiple people were shot to death in an office building in Virginia Beach. The nation's most recent school shooting happened Thursday morning – when a 16-year-old high school student in Santa Clarita, California, opened fire in the campus quad, shooting five classmates and killing two. What if the gun was detected early – so early, the shooter was never able to get inside to hurt anyone? The technology to do that exists, and only WUSA9 was there when it was tested in Northern Virginia.

Cybersecurity machine learning is growing in popularity, according to Jon Oltsik, an analyst with Enterprise Strategy Group Inc. in Milford, Mass. Oltsik attended the recent Black Hat conference, where technology vendors were abuzz with talk of cybersecurity machine learning. ESG research asked 412 respondents about their understanding of artificial intelligence (AI) and cybersecurity machine learning, which revealed that only 30% said they were very knowledgeable on the subject. Only 12% of respondents said their organizations had deployed these systems widely. According to Olstik, the cybersecurity industry sees an opportunity, because only 6% of respondents in surveys said their organizations were not considering AI or machine learning deployments.

Derrick Campana kneels beside Angel Marie, a three-legged mini horse who wears a prosthetic leg made by Campana. This first thing you notice about Derrick Campana's workshop is the machinery. Between the ovens, sewing machines and vacuum tubes lining the walls, it's easy to think Campana is a home economics teacher -- until you catch sight of the dusty paw prints. Campana and and his team use the tools not for domestic projects, but to build prosthetic and orthotic devices for animals. Campana, one of the world's leading experts in animal orthotics, is on a mission to give disabled pets and farm animals options for more mobile lives.

A capsule carrying 5,300 pounds of food, clothing, spare parts, lab equipment, and science experiments arrived at the International Space Station Sunday morning. Astronauts grabbed the vehicle, called Cygnus, with a robotic arm and pulled it to the station for docking. Over the next month the crew will unload its contents while Cygnus remains tethered to the station. Ultimately, the space station crew will reload the empty vessel with about 4,000 pounds of trash and release it to burn up in the atmosphere in mid-November. But before the vehicle destroys itself, an onboard experiment called Spacecraft Fire Experiment-II, or Saffire-II, will intentionally start a small fire to test how zero gravity and limited oxygen affect flame size and the spread of fire.