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

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

Journal of the American Statistical Association: William S. Cleveland, Marylyn E. McGill and Robert McGill, “The shape parameter for a two variable graph” 83 (1988) 289–300

Robotics and autonomous systems

Acoustic reflection inside an enclosed environment is detrimental to human-robot interaction. Reflection may manifest as phantom sources emanating from unknown directions. In effect, a single speaker may falsely manifest as multiple speakers to the robot audition system, impeding the robot's ability to correctly associate the speech command to the actual speaker. Moreover, speech reflection smears the original speech signal due to reverberation. This degrades speech recognition and understanding performance. Conventional robot audition schemes that rely purely on acoustics and spatial information are very sensitive to acoustic reflection which ultimately leads to the failure in human-robot interaction. We propose a method for human-robot interaction robust to the effect of acoustic reflection. First, visual information is utilized and head tracking scheme is employed to reinforce the acoustic information with the visual presence of a prospect user. Second, we employ a model-based sound event identification scheme and scrutinize whether the acoustic information is likely to be speech or non-speech. Using all the information we have gathered, we create a simple rule construct to effectively discriminate the original source (actual speaker) from phantom sources (reflection). Consequently, the corresponding source identified as phantom (reflection) is used to estimate the unwanted smearing for effective suppression via speech enhancement. Experiments are conducted in human-robot interaction setting in which the proposed method outperforms the conventional method.

Speech-based human-robot interaction robust to acoustic reflections in real environment

TAMER has proven to be a powerful interactive reinforcement learning method for allowing ordinary people to teach and personalize autonomous agents' behavior by providing evaluative feedback. However, a TAMER agent planning with UCT---a Monte Carlo Tree Search strategy, can only update states along its path and might induce high learning cost especially for a physical robot. In this paper, we propose to drive the agent's exploration along the optimal path and reduce the learning cost by initializing the agent's reward function via inverse reinforcement learning from demonstration. We test our proposed method in the RL benchmark domain---Grid World---with different discounts on human reward. Our results show that learning from demonstration can allow a TAMER agent to learn a roughly optimal policy up to the deepest search and encourage the agent to explore along the optimal path. In addition, we find that learning from demonstration can improve the learning efficiency by reducing total feedback, the number of incorrect actions and increasing the ratio of correct actions to obtain an optimal policy, allowing a TAMER agent to converge faster.

Improving Interactive Reinforcement Agent Planning with Human Demonstration.

This paper investigates the calibration of a microphone array based robot audition system, namely calibration of microphone array Transfer Functions (TFs). There are mainly two methods to obtain TFs: geometrical calculation and measurement. The geometrical calculation has difficulty in simulating robot- and room-acoustics such as diffraction and reflection properties of a robot's body and a room, and the measurement is accurate but time-consuming and requires expertise on acoustics. Thus, we propose fast and simple on-the-spot calibration of TFs including robot- and room-acoustics. The proposed approach first estimates microphone location and clock-difference by Simultaneous Localization And Mapping (SLAM) using hand clap acoustic signals while a human is walking around a robot. Second, TFs with robot- and room-acoustics are estimated by hand clap acoustic signals and interpolated so that the TFs can be roundly arranged at regular intervals in an online manner. In the evaluation, we calibrated TFs only by 20 hand claps (took only 20 seconds), and the TFs showed considerable improvements in sound source localization and separation compared to geometrically calculated TFs and achieved comparable performance towards the measured TFs which are calibrated by approximately 60 minute recordings.

On-the-spot calibration of microphone array Transfer Functions for robot audition

A speech-processing apparatus includes: a sound source localization unit that localizes a sound source based on an acquired speech signal; and a speech zone detection unit that performs speech zone detection based on localization information localized by the sound source localization unit.

Speech-processing apparatus and speech-processing method

PROBLEM TO BE SOLVED: To estimate a direction of a sound source correctly in a system having a stationary noise sound source. SOLUTION: A direction estimation device 1 of sound source which estimates the direction of the sound source using an eigenvector includes an acoustic signal input means (a microphone array 100) which inputs the acoustic signal, a correlation matrix calculation means (a correlation matrix calculation part 110) which calculates a correlation matrix of the input acoustic signal, and an eigenvector calculation means (an eigenvector calculation part 120) which calculates the eigenvector of the correlation matrix using the calculated correlation matrix. The eigenvector calculation means calculates the eigenvector using the correlation matrix of the input signal and one or more predetermined correlation matrices. COPYRIGHT: (C)2011,JPO&INPIT

Keisuke Nakamura

Papers

Speech-based human-robot interaction robust to acoustic reflections in real environment

Improving Interactive Reinforcement Agent Planning with Human Demonstration.

On-the-spot calibration of microphone array Transfer Functions for robot audition

Speech-processing apparatus and speech-processing method

Direction estimation device of sound source, and direction estimation method of sound source