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

A Protocol Test Generation Procedure.

The symmetric self-electrooptic-effect device (S-SEED), a structure consisting of two p-i-n diodes electrically connected in series and acting as an optically bistable set-reset latch, is discussed. Applications and extensions of this device are also discussed. The devices do not require the critical biasing that is common to most optically bistable devices and thus is more useful for system applications. They have been optically cascaded in a photonic ring counter and have been used to perform different NOR, OR, NAND, and AND logic functions. Using the same device, a differential modulator that generates a set of complementary output beams with a single voltage control lead and a differential detector that gives an output voltage dependent on the ratio of the two optical input powers have been demonstrated. >

Symmetric self-electrooptic effect device : optical set-reset latch, defferential logic gate and differential modulator/detector

Provided is an encoding device which can obtain a sound quality preferable for auditory sense even if the number of information bits is small. The encoding device includes a shape quantization unit (111) having: a section search unit (121) which searches for a pulse for each of bands into which a predetermined search section is divided; and a whole search unit (122) which performs search for a pulse over the entire search section. The shape of an input spectrum is quantized by a small number of pulse positions and polarities. A gain quantization unit (112) calculates a gain of the pulse searched by the shape quantization unit (111) and quantizes the gain for each of the bands.

Encoding device and encoding method

A computer-implemented method for the targeted gathering of tax data for use with tax preparation software includes a computing device presenting to the user a plurality of interview questions or statements. The computing device creates a user profile based on the responses to the interview questions or statements. The computing device identifies highly relevant tax topics based on user profile and executes a user interface manager to automatically generate interview questions or statements on said highly relevant tax topics. The user then confirms whether other tax topics apply to the user. The computing device executes a tax calculation engine of the tax preparation software configured to compute a tax liability or refund amount.

System and method for automated data gathering for tax preparation

A service goal interpreting apparatus for goal-driven semantic service discovery is provided. The service goal interpreting apparatus includes a goal interpretation unit that interprets a goal of at least one service or application provided on the Web, and a goal registration unit that registers the goal interpreted by the goal interpretation unit in a service registry.

Service goal interpreting apparatus and method for goal-driven semantic service discovery

Provided are a method and an apparatus for decoding an audio signal. A method for decoding an audio signal encoded by a layered sinusoidal pulse coding scheme using one or more sinusoidal pulses includes decoding the encoded audio signal, setting a smoothing frequency band of the decoded audio signal according to a layer structure of the layered sinusoidal pulse coding scheme, dividing the smoothing frequency band into one or more subbands, and smoothing the decoded audio signal on a subband-by-subband basis. Accordingly, a decoding operation time can be reduced and the quality of a synthesized signal can be improved by variably setting a frequency band to be smoothed, when decoding an audio signal encoded by a layered sinusoidal pulse coding scheme using one or more sinusoidal pulses.

Method and apparatus for decoding audio signal

We have studied the phase separation and ordering phenomenon of InAlAs epilayers grown on InP substrate by metal–organic chemical vapor deposition (MOCVD). From the intensity and full width half maximum FWHM of double-crystal X-ray diffraction (DCXRD) and photoluminescence (PL), we observed that the structural and optical quality of InAlAs epilayers were improved as growth temperature increased. The band-gap reduction due to phase separation and ordering is 291, 246 and 28 meV in the InAlAs epilayers grown at 565, 615 and 700°C, respectively. The maximum degree of phase separation was obtained from the InAlAs epilayer grown at 565°C. However, the maximum degree of ordering was obtained at the medium growth temperature. A rapid thermal annealing experiment showed a maximum band-gap shift of 78 meV at 880°C for 3 min. Transmission electron microscopy (TEM) showed that the origin of the blue shift of the band-gap was the complete disappearance of ordering and most of the total band-gap reduction (≈3/4) occurred by phase separation.

Observation of phase separation and ordering in the InAlAs epilayer grown on InP at the low temperature

Provided are a main-memory resident object-relational database management system (ORDBMS) and a method of deleting a class instance using the ORDBMS. The method includes obtaining a mutual exclusive lock for a class to be deleted as per the query and a child class inheriting from the class when a query of deleting an instance is input from a database access application program, and deleting the instance from the class and at least one instance related to the instance from its child class using OID reference.

Object-relational database management system and method for deleting class instance for the same

A method and apparatus for controlling voice quality in WLAN is provided. The codec bit rate of wireless terminals is adjusted by collecting channel state information for determining a channel occupation time of wireless terminals connected to an access point, adjusting the codec bit rate of the wireless terminals based on channel occupancy of the wireless terminals with respect to total channel capacity, which is determined using the channel state information, and transmitting the adjusted codec bit rate to each of the wireless terminals.

Byung-Sun Lee

Papers

Service goal interpreting apparatus and method for goal-driven semantic service discovery

Method and apparatus for decoding audio signal

Observation of phase separation and ordering in the InAlAs epilayer grown on InP at the low temperature

Object-relational database management system and method for deleting class instance for the same

Audio codec bit-rate control method for assuring qos of voice in wlan