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

การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

Location sharing and tracking using mobile phones or other wireless devices

A wireless smart card having a personal area network transceiver, such as a Bluetooth transceiver, to couple the wireless smart card with a mobile communication device, and a near field communication (NFC) and radio-frequency identification (RFID) transceiver to couple the wireless smart card to a wireless transaction device, and a transponder with a secure element to allow secure communications between the mobile communication device with the wireless smart card and the wireless smart card and the wireless transaction device is described. The wireless smart card allows, for example, contactless payment through a Bluetooth-enabled mobile communication device without modification to the mobile communication device.

Wireless smart card and integrated personal area network, near field communication and contactless payment system

Corpus linguistics - investigating language structure and use

A mobile communication device is provided for wirelessly transferring information to and from a base unit. The device comprises an RFID module to receive a wake-up communication from the base unit and to send connection parameters back to the base unit. A wireless communication module is included that is responsive to the connection parameters received from the base unit. A microcontroller is coupled between the RFID module and the wireless communication module to wake-up the wireless communication module when the RFID module receives the wake-up communication from the base unit.

System and method for wireless data transfer for a mobile unit

Bluetooth device discovery is a time-intensive phase of the Bluetooth connection-establishment procedure. In this paper we propose a technique that integrates existing IrDA technology with Bluetooth technology to improve the ad hoc connection establishment time of Bluetooth devices. We accomplish this improvement by first establishing an IrDA connection between two devices equipped with both Bluetooth and IrDA capabilities and then exchanging Bluetooth device discovery information via the established IrDA connection. As a result of this cooperative exchange, the devices are able to bypass the time-intensive Bluetooth device discovery procedure. Our research shows that IrDA-assisted Bluetooth connection establishment is up to four times faster than the normal ad hoc Bluetooth connection establishment procedure. In addition, it provides other time-savings in subsequent device selection procedures.

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Rapid heterogeneous ad hoc connection establishment: accelerating Bluetooth inquiry using IrDA

The Poket Doktor is a wireless personal healthcare system that can obtain accurate patient medical information in situations where it may not otherwise be available. The system is designed to provide a flexible, scalable method of storing and communicating critical electronic medical record information using personal handheld electronic devices. The first phase of development has succeeded in: designing the architecture for a wireless, power-efficient smart card to store and communicate medical information incorporating Bluetooth wireless technology with radiofrequency identification wakeup on the smart card to enable a fast wireless connection to a healthcare provider's device; and selecting a platform and creating application software for a handheld computing device used by healthcare providers. The Poket Doktor system assists medical personnel in obtaining accurate patient medical information in situations where it may not otherwise be available. In this manner, Poket Doktor technology will improve the quality of care delivered in emergency situations.

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Enabling remote access to personal electronic medical records

Brooks and others long ago suggested that on average computer programmers write the same number of lines of code in a given amount of time regardless of the programming language used. We examine data collected from the CVS repositories of 9,999 open source projects hosted on SourceForge.net to test this assumption for 10 of the most popular programming languages in use in the open source community. We find that for 24 of the 45 pairwise comparisons, the programming language is a significant factor in determining the rate at which source code is written, even after accounting for variations between programmers and projects.

Do Programming Languages Affect Productivity? A Case Study Using Data from Open Source Projects

In resource-limited mobile computing devices, Bluetooth wireless technology imposes a weighty burden due to inefficient power utilization and a sluggish device discovery process. Buttressing Bluetooth with radiofrequency identification (RFID) technology by performing an operation we call "Rendez-Blue" alleviates these limitations. In the Rendez-Blue process, an RFID signal is used as a cue to "wake-up" a sleeping Bluetooth radio. This ensures that the Bluetooth radio is active only when needed, significantly reducing power consumption. In addition, RFID is used to communicate Bluetooth device information, allowing the user to bypass the traditional 10.24-second discovery process.

/pdf/rf-rendez-blue-reducing-power-and-inquiry-costs-in-bluetooth-53yo90bxkk.pdf

Charles D. Knutson

Papers

System and method for wireless data transfer for a mobile unit

Rapid heterogeneous ad hoc connection establishment: accelerating Bluetooth inquiry using IrDA

Enabling remote access to personal electronic medical records

Do Programming Languages Affect Productivity? A Case Study Using Data from Open Source Projects

RF Rendez-Blue: reducing power and inquiry costs in Bluetooth-enabled mobile systems