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

In this paper, we present a method of communicating affects from a remote user through the telepresence robot Haru. In this preliminary work, we transform the traditional mode of communicating text messages with emojis in the smartphone domain to the robot domain through robomojis- a hardware rendition of emojis. First we analyze human affects and expressions and synthesized these through the design of robomojis. Based on the telepresence robot Haru platform, we built a communication module that enables a remote user with a smartphone to send text and emoji messages to the robot. The robot then communicates these affectively through the rendition of robomojis. By exploiting the robot’s rich modality and through design, our preliminary results show that robomojis deliver better rendition than emojis. Although participants prefer the use of smartphones over telepresence robot due to convenience, our findings also show that they are willing to use the robomoji features in a telepresence robot such as Haru. This could enhance further the role of social robots as telepresence robots and open the possibility of integrating them to the traditional communication ecosystem.

Emoji to Robomoji: Exploring Affective Telepresence Through Haru.

An information presentation device includes an audio signal input unit configured to input an audio signal, an image signal input unit configured to input an image signal, an image display unit configured to display an image indicated by the image signal, a sound source localization unit configured to estimate direction information for each sound source based on the audio signal, a sound source separation unit configured to separate the audio signal to sound-source-classified audio signals for each sound source, an operation input unit configured to receive an operation input and generates coordinate designation information indicating a part of a region of the image, and a sound source selection unit configured to select a sound-source-classified audio signal of a sound source associated with a coordinate which is included in a region indicated by the coordinate designation information, and which corresponds to the direction information.

Information presentation device associated with sound source separation

PROBLEM TO BE SOLVED: To provide an electromagnetic testing apparatus that can radiate complex electromagnetic waves in EMS testing.SOLUTION: An electromagnetic testing apparatus comprises a plurality of antenna elements 14 that radiate electromagnetic waves when a high-frequency signal is inputted, a plurality of signal generators 31 to 37 each connected to the input terminal of one or another of the multiple antenna elements 14 and generating a high-frequency signal, and a measuring gauge 26. The measuring gauge 26 so controls each of the signal generators 31 to 37 as to cause an electromagnetic wave of a frequency, preset for each individual antenna element 14, to be radiated. Then, the durability of a given item of electronic equipment is checked (EMS test) with electromagnetic waves from outside on the basis of an output signal from the electronic equipment disposed in a sample. This configuration enables diverse noise environments to be reproduced when conducting an EMS test.

Electromagnetic testing apparatus

This paper presents a method for estimating the number, as well as the activity periods of spatially distributed sound sources using an uncalibrated microphone array. This methodology is applied for the purposes of speaker diarization. In general, speaker diarization has difficulty with: 1) estimating the number of sound sources (speakers), and 2) activity detection of multiple sound sources including overlap of utterances. Several microphone array based techniques have already tackled these challenges. However, existing methods mainly assume that the steering vectors for the microphone array are calibrated in advance to identify sound sources, which is difficult to satisfy when ad-hoc or flexible microphone arrays are used. Thus our approach estimates the number of sound sources blindly in two steps. First, Time Delay of Arrival (TDOA) of the observed signal is clustered. Second, the sound source activity is detected by clustering the long-term spatial spectrum using the TDOA based steering vector for each cluster. The validity of the algorithm is confirmed by both synthesized signals and a real-world flexible microphone array application.

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

PROBLEM TO BE SOLVED: To provide a method and device for calibrating a smoke detector and to enable the miniaturization of the device and the stable and rapid calibration of the sensitivity of the device by simple constitution.SOLUTION: The calibration device 1 includes a smoke producing part 2 having predetermined volume and a sensor arranging part 3 both of which are adjacent to each other through a partition 4. A smoke generator 21 is arranged in the smoke producing part 2 and the smoke detector 5 can be arranged on the sensor arranging part 3, removably. The partition 4 has an opening (not shown). and a movable valve for opening and closing the opening and the flow of gas between the smoke producing part 2 and the sensor arranging part 3 is cut off by closing the opening by the valve while the gas is allowed to freely flow between the smoke producing part 2 and the sensor arranging part 3 by operating the valve to open the opening of the partition 4. Calibration is performed in a stationary state after the gas detector is filled with smoke of definite concentration, so that stable and precise calibration can be repeatedly performed. The smoke producing part 2 and the sensor arranging part 3 may be volumetrically kept to a necessary minimum and the whole of the calibration device 1 can be miniaturized.

Keisuke Nakamura

Papers

Emoji to Robomoji: Exploring Affective Telepresence Through Haru.

Information presentation device associated with sound source separation

Electromagnetic testing apparatus

Blind spatial sound source clustering and activity detection using uncalibrated microphone array

Method and device for calibrating smoke detector