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Using Multivariate Statistics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.

/pdf/science-and-technology-roadmap-for-graphene-related-two-4q9m7czlkc.pdf

Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems

Multiple applications request data from multiple storage units over a computer network. The data is divided into segments and each segment is distributed randomly on one of several storage units, independent of the storage units on which other segments of the media data are stored. At least one additional copy of each segment also is distributed randomly over the storage units, such that each segment is stored on at least two storage units. This random distribution of multiple copies of segments of data improves both scalability and reliability. When an application requests a selected segment of data, the request is processed by the storage unit with the shortest queue of requests. Random fluctuations in the load applied by multiple applications on multiple storage units are balanced nearly equally over all of the storage units. This combination of techniques results in a system which can transfer multiple, independent high-bandwidth streams of data in a scalable manner in both directions between multiple applications and multiple storage units.

Computer system and process for transferring multiple high bandwidth streams of data between multiple storage units and multiple applications in a scalable and reliable manner

Data generated as a side effect of game play also solves computational problems and trains AI algorithms

https://dl.acm.org/doi/pdf/10.1145/1378704.1378719

Designing games with a purpose

The Garnet research project, which is creating a set of tools to aid the design and implementation of highly interactive, graphical, direct-manipulation user interfaces, is discussed. Garnet also helps designers rapidly develop prototypes for different interfaces and explore various user-interface metaphors during early product design. It emphasizes easy specification of object behavior, often by demonstration and without programming. Garnet contains a number of different components grouped into two layers. The Garnet Toolkit (the lower layer) supplies the object-oriented graphics system and constraints, a set of techniques for specifying the objects' interactive behavior in response to the input devices, and a collection of interaction techniques. On top of the Garnet Toolkit layer are a number of tools to make creating user interfaces easier. The components of both layers are described. >

/pdf/garnet-comprehensive-support-for-graphical-highly-1vgwme23x6.pdf

Garnet: comprehensive support for graphical, highly interactive user interfaces

An On-Line Algorithm for Real-Time Accompaniment

We describe a method that aligns polyphonic audio recordings of music to symbolic score information in standard MIDI files without the difficult process of polyphonic transcription. By using this method, we can search through a MIDI database to find the MIDI file corresponding to a polyphonic audio recording.

/pdf/polyphonic-audio-matching-and-alignment-for-music-retrieval-1qcxnfmi9z.pdf

Polyphonic audio matching and alignment for music retrieval

Annotations of audio files can be used to search and index music and sound databases, provide data for system evaluation, and generate training data for machine learning. Unfortunately, the cost of obtaining a comprehensive set of annotations manually is high. One way to lower the cost of labeling is to create games with a purpose that people will voluntarily play, producing useful metadata as a by-product. TagATune is an audio-based online game that aims to extract descriptions of sounds and music from human players. This paper presents the rationale, design and preliminary results from a pilot study using a prototype of TagATune to label a subset of the FreeSound database.

/pdf/tagatune-a-game-for-music-and-sound-annotation-4740tokuxc.pdf

TagATune: A Game for Music and Sound Annotation.

Much of the work on perception and understanding of music by computers has focused on low-level
perceptual features such as pitch and tempo. Our work demonstrates that machine learning can be
used to build effective style classifiers for interactive performance systems. We also present an analysis explaining why these techniques work so well when hand-coded approaches have consistently
failed. We also describe a reliable real-time performance style classifier.

Roger B. Dannenberg

Papers

Garnet: comprehensive support for graphical, highly interactive user interfaces

An On-Line Algorithm for Real-Time Accompaniment

Polyphonic audio matching and alignment for music retrieval

TagATune: A Game for Music and Sound Annotation.

A Machine Learning Approach to Musical Style Recognition