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

Selecting an item of interest on smartwatches can be tedious and time-consuming as it involves a series of swipe and tap actions. We present PageFlip, a novel method that combines into a single action multiple touch operations such as command invocation and value selection for efficient interaction on smartwatches. PageFlip operates with a page flip gesture that starts by dragging the UI from a corner of the device. We first design PageFlip by examining its key design factors such as corners, drag directions and drag distances. We next compare PageFlip to a functionally equivalent radial menu and a standard swipe and tap method. Results reveal that PageFlip improves efficiency for both discrete and continuous selection tasks. Finally, we demonstrate novel smartwatch interaction opportunities and a set of applications that can benefit from PageFlip.

PageFlip: Leveraging Page-Flipping Gestures for Efficient Command and Value Selection on Smartwatches

Remote photoplethysmography (RPPG) is a technique to measure the physiological signs (such as Heart Rate) remotely. Compared with the conventional photoplethysmography (PPG), the RPPG provides long term monitoring without contact equipment and makes the application possible and comfortable in daily life. Heart Rate Variability (HRV) is a medical index which is calculated as the interval of the heartbeats. It has been proved that the HRV can be used as a biomarker for the autonomic nervous system (ANS) so that the features of HRV are possible to be utilized to detect the human stress and other emotion states [1] [2]. To make the RPPG method works more effectively in human emotion states detection, the measurement of HRV should be improved. In this paper, we propose to use the slope sum function (SSF) to improve the interbeat interval (IBI) detection. The performance of this new method was evaluated with the HRV features which can be used for emotion detection. The results showed that this new method has improved the accuracy of the HRV measurement in both frequency domain and time domain.

An Improvement for Video-based Heart Rate Variability Measurement

This paper addresses sound source detection in an outdoor environment using a quadcopter with a microphone array. As the previously reported method has a high computational cost, we proposed a sound source detection algorithm called multiple signal classification based on incremental generalized singular value decomposition (iGSVD-MUSIC) that detects the sound source location and temporal activity at low computational cost. In addition, to relax the estimation error problem of a noise correlation matrix that is used in iGSVD-MUSIC, we proposed correlation matrix scaling (CMS) to achieve soft whitening of noise. As CMS requires a parameter to decide the degree of whitening, we analyzed the optimal value of the parameter by using numerical simulation. The prototype system based on the proposed methods was evaluated with two types of microphone arrays in an outdoor environment. The experimental results showed that the proposed iGSVD-MUSIC-CMS significantly improves sound source detection performance, and the prototype system achieves real-time processing. Moreover, we successfully clarified the behavior of the CMS parameter by using a numerical simulation in which the empirically-obtained optimal value corresponded with the analytical result.1

Outdoor Sound Source Detection Using a Quadcopter with Microphone Array

In this paper we present a method in room transfer function (RTF) estimation, employed specifically for dereverberation in hands-free human-robot communication.We introduce a radial distance compensation scheme which significantly improved the RTF estimate robust to the speech power variation due to changes in speaker's radial position. The proposed method is implemented in two levels; first, waveform-level compensation is executed to reflect the change in power caused by the change of radial position to the RTF. We generated possible RTF estimates within a close neighbourhood based on curve fitting. Then, we select among these estimates the optimal RTF based on acoustic model likelihood criterion, the same criterion employed in automatic speech recognition (ASR) systems. The latter is referred to as acoustic model-level compensation, which links the generated RTF to the ASR. We note that in ASR application, both waveform and acoustic models play an important role in achieving optimal performance. Thus, the synergistic effect of the two processes guarantee ASR performance improvement when used in conjunction with our ASR-based dereverberation scheme. Experimental evaluation show robustness in recognition performance when used in hands-free human-robot communication environment.

/pdf/hands-free-human-robot-communication-robust-to-speaker-s-3u4ufbegf5.pdf

Hands-free human-robot communication robust to speaker's radial position

Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it provides remote physiological measurements without expensive hardware and improves comfort for long term monitoring. RPPG estimation methods use the spatially averaged RGB values of pixels in a Region Of Interest (ROI) to generate a temporal RGB signal. The selection of ROI is a critical first step to obtain reliable pulse signals and must contain as many skin pixels as possible with a low percentage of non-skin pixels. In this paper, we experimentally compare seven ROI segmentation methods in the perspective of heart rate (HR) measurements with dedicated metrics. The algorithms are compared using our in-house database UBFC-RPPG, comprising of 53 videos specifically geared towards rPPG analysis.

/pdf/comparison-of-region-of-interest-segmentation-methods-for-4qcesm14df.pdf

Keisuke Nakamura

Papers

PageFlip: Leveraging Page-Flipping Gestures for Efficient Command and Value Selection on Smartwatches

An Improvement for Video-based Heart Rate Variability Measurement

Outdoor Sound Source Detection Using a Quadcopter with Microphone Array

Hands-free human-robot communication robust to speaker's radial position

Comparison of Region of Interest Segmentation Methods for Video-Based Heart Rate Measurements