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Category Archives: Machine Learning

– Retracing the evolution of classical music with machine learning – Design Products & Applications

05 February 2021

Researchers in EPFLs Digital and Cognitive Musicology Lab in the College of Humanities used an unsupervised machine learning model to reveal how modes such as major and minor have changed throughout history.

Many people may not be able to define what a minor mode is in music, but most would almost certainly recognise a piece played in a minor key. Thats because we intuitively differentiate the set of notes belonging to the minor scale which tend to sound dark, tense, or sad from those in the major scale, which more often connote happiness, strength, or lightness.

But throughout history, there have been periods when multiple other modes were used in addition to major and minor or when no clear separation between modes could be found at all.

Understanding and visualising these differences over time is what Digital and Cognitive Musicology Lab (DCML) researchers Daniel Harasim, Fabian Moss, Matthias Ramirez, and Martin Rohrmeier set out to do in a recent study, which has been published in the open-access journal Humanities and Social Sciences Communications. For their research, they developed a machine learning model to analyze more than 13,000 pieces of music from the 15th to the 19th centuries, spanning the Renaissance, Baroque, Classical, early Romantic, and late-Romantic musical periods.

We already knew that in the Renaissance [1400-1600], for example, there were more than two modes. But for periods following the Classical era [1750-1820], the distinction between the modes blurs together. We wanted to see if we could nail down these differences more concretely, Harasim explains.

Machine listening (and learning)

The researchers used mathematical modelling to infer both the number and characteristics of modes in these five historical periods in Western classical music. Their work yielded novel data visualizations showing how musicians during the Renaissance period, like Giovanni Pierluigi da Palestrina, tended to use four modes, while the music of Baroque composers, like Johann Sebastian Bach, revolved around the major and minor modes. Interestingly, the researchers could identify no clear separation into modes of the complex music written by Late Romantic composers, like Franz Liszt.

Harasim explains that the DCMLs approach is unique because it is the first time that unlabelled data have been used to analyse modes. This means that the pieces of music in their dataset had not been previously categorized into modes by a human.

We wanted to know what it would look like if we gave the computer the chance to analyse the data without introducing human bias. So, we applied unsupervised machine learning methods, in which the computer 'listens' to the music and figures out these modes on its own, without metadata labels.

Although much more complex to execute, this unsupervised approach yielded especially interesting results which are, according to Harasim, more cognitively plausible with respect to how humans hear and interpret music.

We know that musical structure can be very complex and that musicians need years of training. But at the same time, humans learn about these structures unconsciously, just as a child learns a native language. Thats why we developed a simple model that reverse engineers this learning process, using a class of so-called Bayesian models that are used by cognitive scientists, so that we can also draw on their research.

From class project to publicationand beyond

Harasim notes with satisfaction that this study has its roots in a class project that he and his co-authors Moss and Ramirez did together as students in EPFL professor Robert Wests course, Applied Data Analysis. He hopes to take the project even further by applying their approach to other musical questions and genres.

For pieces within which modes change, it would be interesting to identify exactly at what point such changes occur. I would also like to apply the same methodology to jazz, which was the focus of my PhD dissertation because the tonality in jazz is much richer than just two modes.

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Machine Learning and Artificial Intelligence in Healthcare Market 2021 inclining trends with NVIDIA Corporation, Intel Corporation, GENERAL ELECTRIC…

Travel Guard has specific cruise insurance policies, which makes it simpler than trying to find an add-on. If youre getting a quote online, theyll ask you to specify if youre taking a plane, a cruise, or both. They cover any emergency travel assistance, trip interruption, delay, or cancellation.

Cruise travel insurance secures non-refundable investments related to your trip. It reimburses you if you have to cancel your international cruise unexpectedly prior to your departure. It also provides medical coverage for unexpected injuries and illnesses. Cruise travel insurance policies provide medical coverage while you are on a holiday. A cancellation after this can mean a huge financial loss, but a cruise travel insurance policyholder shall be covered for cancellation or postponement of trips.

The aim of the report is to equip relevant players in deciphering essential cues about the various real-time market based developments, also drawing significant references from historical data, to eventually present a highly effective market forecast and prediction, favoring sustainable stance and impeccable revenue flow despite challenges such as sudden pandemic, interrupted production and disrupted sales channel in the Cruise Travel Insurance market.

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Key players profiled in the report includes:

Allianz, AIG, Munich RE, Generali, Tokio Marine, Sompo Japan, CSA Travel Protection, AXA, Pingan Baoxian, Mapfre Asistencia, USI Affinity, Seven Corners, Hanse Merkur, MH Ross, STARR

Market Segmentation by type:

Market Segmentation by application:

This report is well documented to present crucial analytical review affecting the Cruise Travel Insurance market amidst COVID-19 outrage. The report is so designed to lend versatile understanding about various market influencers encompassing a thorough barrier analysis as well as an opportunity mapping that together decide the upcoming growth trajectory of the market. In the light of the lingering COVID-19 pandemic, this mindfully drafted research offering is in complete sync with the current ongoing market developments as well as challenges that together render tangible influence upon the holistic growth trajectory of the Cruise Travel Insurance market.

Besides presenting a discerning overview of the historical and current market specific developments, inclined to aid a future-ready business decision, this well-compiled research report on the Cruise Travel Insurance market also presents vital details on various industry best practices comprising SWOT and PESTEL analysis to adequately locate and maneuver profit scope. Therefore, to enable and influence a flawless market-specific business decision, aligning with the best industry practices, this specific research report on the market also lends a systematic rundown on vital growth triggering elements comprising market opportunities, persistent market obstacles and challenges, also featuring a comprehensive outlook of various drivers and threats that eventually influence the growth trajectory in the Cruise Travel Insurance market.

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Global Cruise Travel Insurance Geographical Segmentation Includes:

North America (U.S., Canada, Mexico)

Europe (U.K., France, Germany, Spain, Italy, Central & Eastern Europe, CIS)

Asia Pacific (China, Japan, South Korea, ASEAN, India, Rest of Asia Pacific)

Latin America (Brazil, Rest of L.A.)

Middle East and Africa (Turkey, GCC, Rest of Middle East)

Some Major TOC Points:

Chapter 1. Report Overview

Chapter 2. Global Growth Trends

Chapter 3. Market Share by Key Players

Chapter 4. Breakdown Data by Type and Application

Chapter 5. Market by End Users/Application

Chapter 6. COVID-19 Outbreak: Cruise Travel Insurance Industry Impact

Chapter 7. Opportunity Analysis in Covid-19 Crisis

Chapter 9. Market Driving Force

And More

In this latest research publication a thorough overview of the current market scenario has been portrayed, in a bid to aid market participants, stakeholders, research analysts, industry veterans and the like to borrow insightful cues from this ready-to-use market research report, thus influencing a definitive business discretion. The report in its subsequent sections also portrays a detailed overview of competition spectrum, profiling leading players and their mindful business decisions, influencing growth in the Cruise Travel Insurance market.

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http://www.reportconsultant.com

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Machine Learning and Artificial Intelligence in Healthcare Market 2021 inclining trends with NVIDIA Corporation, Intel Corporation, GENERAL ELECTRIC...

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NTUC LearningHub Survey Reveals Accelerated Business Needs In Cloud Computing And Machine Learning Outpacing Singapore Talent Supply; Skills Gap A…

SINGAPORE -Media OutReach-5 February2021 -Despite majority of Singapore employers(89%) reporting that the COVID-19 pandemic has accelerated the adoption of cloudcomputing and Machine Learning (ML) in their companies, obstacles abound. Singaporebusiness leaders say that the largest hindrance to adopting cloud computing andML technologies is the shortage of relevant in-house IT support (64%), amongstother reasons such as 'employees do not have the relevant skill sets' (58%) and'the lack of financial resources' (46%).

alt="NTUC LearningHub Survey Reveals Accelerated Business Needs In Cloud Computing And Machine Learning Outpacing Singapore Talent Supply; Skills Gap A Hindrance To Implementing These Technologies"

These are some ofthe key findings from the recently launched NTUC LearningHub (NTUC LHUB)Industry Insights report on cloud computing and ML in Singapore. The report is basedon in-depth interviews with industry experts, such as Amazon Web Services (AWS)and NTUC LHUB, and a survey with 300 hiring managers across industries inSingapore.

While organisationsare keen to adopt cloud computing and ML to improve the company's businessperformance (64%), obtain business insights from Big Data (59%) and performmundane or tedious tasks (53%), a third of Singapore employers (32%) say theircompanies have insufficient talent to implement cloud computing and MLtechnologies.

To overcome thisshortage, companies say they have been upskilling employees that have relevantskill sets/ roles (55%), and reskilling employees that have completelydifferent skill sets/ roles (44%). In a further show of how organisations werewilling to take steps to overcome this skills gap, three in five (61%) stronglyagree or agree that they will be open to hiring individuals with relevantmicro-credentials, even if these candidates has no relevant experience oreducation degrees.

Looking to thefuture, four in five employers (81%) agree or strongly agree that ML will bethe most in-demand Artificial Intelligence (AI) skill in 2021. Meanwhile, sevenout of 10 surveyed (70%) indicated they will be willing to offer a premium fortalent with AI and ML skills.

"The report reinforces the growing demand for a cloud-skilled workforce inSingapore, and the critical need to upskill and reskill local talent", said TanLee Chew, Managing Director, ASEAN, Worldwide Public Sector, AWS. "Thecollaboration across government, businesses, education and traininginstitutions will be instrumental in helping Singapore employers address theseskills gaps. AWS will continue to collaborate with training providers like NTUCLearningHub to make skills training accessible to help Singaporeans, fromstudents to adult learners, to remain relevant today and prepare for the future."

NTUC LHUB's Head ofICT, Isa Nasser also adds, "While much of the talent demand encompasses technicalpositions such as data scientists and data engineers, businesses are alsolooking for staff to pick up practical ML and data science skills sets that canbe applied to their existing work. Thatis why in today's digital age, most professionals would benefit greatly frompicking up some data science skills to enable them to deploy ML applicationsand use cases in their organization. We highly urge workers to get started on equipping themselveswith ML skills, including understanding the core concepts of data science, aswell as familiarising themselves on the use of cloud or ML platforms such as AmazonSageMaker."

To download theIndustry Insights: Cloud Computing and ML report, visit

https://www.ntuclearninghub.com/machine-learning-cloud.

NTUCLearningHub is the leading Continuing Education and Training provider in Singapore,which aims to transform the lifelong employability of working people. Since ourcorporatisation in 2004, we have been working employers and individual learnersto provide learning solutions in areas such as Cloud, Infocomm Technology,Healthcare, Employability & Literacy, Business Excellence, Workplace Safety& Health, Security, Human Resources and Foreign Worker Training.

Todate, NTUC LearningHub has helped over 25,000 organisations and achieved over2.5 million training places across more than 500 courses with a pool of over460 certified trainers. As a Total Learning Solutions provider toorganisations, we also forge partnerships and offer a wide range of relevantend-to-end training solutions and work constantly to improve our trainingquality and delivery. In 2020, we have accelerated our foray into onlinelearning with our Virtual Live Classes and, through working with best-in-classpartners such as IBM, DuPont Sustainable Solutions and GO1, asynchronous onlinecourses.

For moreinformation, visitwww.ntuclearninghub.com.

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Machine Learning To Bring A Transformation In Software Testing – CIO Applications

The test automation effort will continue to accelerate. Surprisingly, a lot of businesses do have manual checks in their distribution pipeline, but you can't deliver quickly if you have humans on the vital path of the supply chain, slowing things down.

FREMONT, CA: Over the last decade, there has been an unwavering drive to deliver applications faster. Automated testing has emerged as one of the most relevant technologies for scaling DevOps, businesses are spending a lot of time and effort to develop end-to-end software delivery pipelines, and containers and their ecosystem are keeping up with their early promise.

Testing is one of the top DevOps monitors that companies can use to ensure that their consumers have a delightful brand experience. Others include access management, logging, traceability and disaster recovery.

Quality and access control are preventive controls, while others are reactive. In the future, there will be a growing emphasis on consistency because it prevents consumers from having a bad experience. So delivering value quicklyor better still delivering the right value quickly at the right quality levelis the main theme that everyone will see this year and beyond.

Here are the five key trends in 2021:

Automation of exams

The test automation effort will continue to accelerate. Surprisingly, a lot of businesses do have manual checks in their distribution pipeline, but you can't deliver quickly if you have humans on the vital path of the supply chain, slowing things down.

Automation of manual tests is a long process that takes dedicated engineering time. While many companies have at least some kind of test automation, much needs to be done. That's why automated testing will remain one of the top trends in the future.

DevOps-driven data

Over the past six to eight years, the industry has concentrated on linking various resources through the development of robust distribution pipelines. Each of these tools produces a significant amount of data, but the data is used minimally, if at all.

The next stage is to add the smarts to the tooling. Expect to see an increased focus on data-driven decision-making by practitioners.

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REACH and Millennium Systems International Partner to offer Machine Learning Driven Booking Automation to the MeevoXchange Marketplace – PRNewswire

REACH is available in award-winning Millennium System International's scheduling software product, Meevo 2, and serves thousands of beauty businesses in over 30 countries."We are thrilled to announce another Meevo 2 business building integration offering within our MeevoXchange marketplace REACH by Octopi. REACH delivers the AI-powered smart scheduling features to help keep our salons and spas booked and growing. This partnership aligns with our strategic goals for our award-winning software Meevo 2 as we continuously add value to our platform and ultimately our salon and spa customers," says CEO John Harms, Millennium Systems International.

"REACH is so special because it requires virtually no setup or upkeep as it follows your existing Meevo 2 online booking settings. REACH plays 'matchmaker' by connecting your clients that are due and overdue with open spaces in your Meevo 2 appointment book over the next few days, automatically. It has taken us years of research and development to create such successful and exciting tool that will begin to show value to your business starting on day one!" CEO Patrick Blickman, REACH by Octopi

Performance Guarantee and Affordability

The platform includes the REACH Revenue Guarantee thatensures each location will see a minimum of $600-$1400 in new booking revenue every month. There are never any contracts or commitments with REACH. Simply turn it on and let it start filling your Meevo 2 appointment book. Pricing starts at $149/month.

About REACH by OCTOPI

REACH was founded to make the client booking experience easier and far more automated for the health and beauty businesses we serve. Headquartered in Scottsdale, Arizona; REACH is built on decades of consolidated industry and channel expertise. Visitwww.octopi.com/reach

About Millennium Systems International:

Millennium Systems International has been a leading business management software for the salon, spa and wellness industry for more than three decades. The award-winning Meevo 2 platform provides a true cloud-based business management software that is HIPAA compliant and fully responsive, so users can gain complete access using any device, built by wellness and beauty veterans exclusively for the wellness and beauty industry. Visit https://www.millenniumsi.com

SOURCE Octopi

octopi.com

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REACH and Millennium Systems International Partner to offer Machine Learning Driven Booking Automation to the MeevoXchange Marketplace - PRNewswire

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Can Machine Learning be the Best Remedy in the Education Sector? – Analytics Insight

The classrooms in present era are not only expanding to use more technologies and digital tools but they are also engaging in machine learning

Technology in the classroom is becoming more and more popular as we pass through the 21st century. Laptops are replacing our textbooks, and on our smart phones, we can study just about everything we want. Social media has become ubiquitous, and the way we use technology has changed the way we live our lives fully.

Technology has become the core component of distance education programs. It enhances teachers and students to digitally interconnect and exchange material and student work, retaining a human link, which is important for the growth of young minds. Enhanced connections and customized experience can allow educators torecognizeopportunities for learning skills and enhance the potential of a student.

Hence, the classrooms in present era are not only expanding to use more technologies and digital tools but they are also engaging in machine learning.

Machine learning is an artificial intelligence (AI) element, which lets machines or computers learn from all previous knowledge and make smart decisions. The architecture for machine learning involves gathering and storing a rich collection of information and turning it into a standardized knowledge base for various uses in different fields. Educators could save time in their non-classroom practices in the field of education by concentrating on machine learning.

For instance, teachers may use virtual helpers to work for their students directly from home. This form of assistance helps to boost the learning environment of students and can promote growth and educational success.

According to ODSC, Last years report by MarketWatch has revealed that Machine Learning in education will remain one of the top industries to drive investment, with the U.S. and China becoming the top key players by 2030. Major companies, like Google and IBM, are getting involved in making school education more progressive and innovative.

Analyzing all-round material

By making the content more up-to-date and applicable to an exact request, the use of machine learning in education aims to bring the online learning sector to a new stage. How? ML technologies evaluate the content of courses online and help to assess whether the quality of the knowledge presented meets the applicable criteria. On the other hand, know how users interpret the data and understand what is being explained. Users then obtain the data according to their particular preferences and expertise, and the overall learning experience increases dramatically.

Customized Learning

This is the greatest application of machine learning. It is adaptable and it takes care of individual needs. Students are able to guide their own learning through this education system. They can have theirown speed and decide what to study and how to learn. They can select the topics they are interested in, the instructor they want to learn from, and what program they want to pursue, expectations and trends.

Effective Grading

In education, there is another application of machine learning that deals with grades and scoring. Since the learning skills of a large number of students are expressed in each online course, grading them becomes a challenge. ML technology makes the grading process a few seconds problem. In this context, we talk more about the exact sciences. There are places where teachers cannot be replaced by computers, but even in such situations, they can contribute to enhance current approaches of grading and evaluation.

According to TechXplore, Researchers at University of Tbingen and Leibniz Institute fr Wissensmedien in Germany, as well as University of Colorado Boulder, have recently investigated the potential of machine-learning techniques for assessing student engagement in the context of classroom research. More specifically, they devised a deep-neural-network-based architecture that can estimate student engagement by analyzing video footage collected in classroom environments.

They also mentioned that, We used camera data collected during lessons to teach a deep-neural-network-based model to predict student engagement levels, Enkelejda Kasneci the leading HCI researcher in the multidisciplinary team that carried out the study, told TechXplore. We trained our model on ground-truth data (e.g., expert ratings of students level of engagement based on the videos recorded in the classroom). After this training, the model was able to predict, for instance, whether data obtained from a particular student at a particular point in time indicates high or low levels of engagement.

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