This paper addresses two issues toward practical use of wearable electroencephalogram (EEG) measurement devices. Ocular (eye movement and blink) artifacts often contaminate EEGs and deteriorate the performance of EEG-based brain–computer interfaces (BCIs). Although wearable consumer-grade EEG devices with single electrode allow users to operate BCIs conveniently in daily lives, it remains a challenging issue to attenuate ocular artifacts from single-channel measurements without spatial information. Existing ocular artifact reduction methods are, however, not simple enough for single-channel EEG data in the sense that they require an additional reference channel and/or pre-processing for artifact segment detection. Another issue is how to assess the performance of artifact reduction; the existing studies have used their own datasets that are not accessible from other researchers. Then, this paper makes two major contributions. (1) This paper proposes a novel ocular artifact reduction method, multi-scale dictionary learning (MSDL), which operates under single-channel measurements and without artifact segment detection. (2) We also develop a semi-simulation setting for quantitative evaluation with a publicly available EEG dataset. In particular, we employed BCI Competition IV Dataset 2a, on which the proposed method was compared with state-of-art methods. The proposed technique showed the best performance for recovering artifact-reduced waveforms from single-channel data compared to the other artifact reduction methods. The Matlab scripts for semi-simulation data generation and single-channel artifact reduction are available on GitHub.

Reviews written by customers on the Web can influence many people when they decide what to do. Offensive or irrelevant reviews are often posted to review services (e.g. TripAdvisor1and Glassdoor2), and they can make people displeased and ruin services' reputation. To avoid this, review service providers issue guidelines that define what are inappropriate reviews and employ human workers to manually remove reviews violating guideline. Such manual operations incur high costs and human filtering results may vary; then, automatic filtering is desirable. Unfortunately, although several filtering methods are available, their accuracy and efficiency are still not enough to work well on actual review services because of their costs, complexities and reviews' noisiness. In this paper, we introduce a simple, accurate, and efficient method that detects whether a review violates guidelines or not, using logistic regression models with features based on word n-gram. We show through experiments on real review data that the method works well under practical and difficult situations. The method can be applied to any review services, and can eliminate the costs of manual operations.

To efficiently process and understand a large amount of incoming visual information from first-person perspective (i.e. egocentric vision), predicting human gaze is important. However, even though people continuously gaze in noisy environments, most existing gaze prediction methods mainly use image saliency, which is sensitive to noise in the real-world. To address this issue, we propose a sparse coding-based saliency detection method for gaze prediction. Our model uses a cost function with the 10 norm as a sparse constraint that can control the area of visual saliency in response to the contents of egocentric vision in intuitive and consistent ways. Moreover, we use canonical correlation analysis (CCA) to combine different types of features for reducing noise and the computational complexity. We also utilize the temporal continuity of image frames when defining our saliency. Experiments using a real-world gaze dataset show that our proposed approach outperforms the state-of-the-art algorithms on gaze prediction in egocentric videos.

We propose a physical-world question-answering (QA) method, where the system answers a text question about the physical world by searching a given sequence of sentences about daily-life episodes. To address various information needs in a physical world situation, the physical-world QA methods have to generate mixed-type responses (e.g. word sequence, word set, number, and time as well as a single word) according to the content of questions, after reading physical-world event stories. Most existing methods only provide words or choose answers from multiple candidates. In this paper, we use multiple decoders to generate a mixed-type answer encoding daily episodes with a memory architecture that can capture short- and long-term event dependencies. Results using house-activity stories show that the use of multiple decoders with memory components is effective for answering various physical-world QA questions.

Functional near-infrared spectroscopy (fNIRS) offers a convenient means to measure human brain activity in real environments, driving many applications in neuroscience, medicine, and engineering. However, it is well known that fNIRS observations are always contaminated by scalp blood flow (SBF), which is not from the brain but from the skin. Several methods have been proposed for extracting cerebral blood flow (CBF), which reflects brain activities. The issue we tackled in this paper is how to evaluate the quality of CBF extraction for designing effective fNIRS applications. The standard way is to simultaneously measure reference signals such as SBF by laser Doppler blood flow meters and compare extracted CBF with them. This hardware approach requires additional instruments to be installed on the fNIRS device, declining the convenience of fNIRS recording in real environments. Then, in this paper we proposed a no-reference metric for evaluating the performance of CBF extraction. The proposed metric is based on decoding analysis of only fNIRS data and does not require additional instruments. The fundamental idea is to utilize the difference of SBF patterns in two experimental conditions; therefore an inherent limitation of the proposed metric is that it does not directly apply to single-condition data. We tested the proposed metric with popular CBF extraction techniques, namely, principal component analysis (PCA) and independent component analysis (ICA), and observed that these techniques may have room for improvement. The no-reference metric would be useful for evaluating CBF extraction techniques and promote the development of fNIRS applications.

Selecting authentic scenes about activities of daily living (ADL) is useful to support our memory of everyday life. Key-frame extraction for first-person vision (FPV) videos is a core technology to realize such memory assistant. However, most existing key-frame extraction methods have mainly focused on stable scenes not related to ADL and only used visual signals of the image sequence even though the activities usually associate with our visual experience. To deal with dynamically changing scenes of FPV about daily activities, integrating motion and visual signals are essential. In this paper, we present a novel key-frame extraction method for ADL, which integrates multi-modal sensor signals to temper noise and detect salient activities. Our proposed method projects motion and visual features to a shared space by a probabilistic canonical correlation analysis and selects key frames there. The experimental results using ADL datasets collected in a house suggest that our key-frame extraction technique running in the shared space improves the precision of extracted key frames and the coverage of the entire video.

First-person videos (FPVs) in daily living help us to memorize our life experience and information systems to process daily activities. Summarizing FPVs into key frames that represent the entire data would allow us to remember our memory in the past and computers to efficiently process the data. However, most video summarization approaches only use visual information, even though our daily activities consist of multiple modalities such as movements and sounds. FPVs are not as stable as movies or sport scenes since the camera attached to the head shakes frequently, and key frame extraction methods rely only on video frames do not always produce satisfactory results. In this paper, we introduce a novel key frame extraction method for FPVs using multiple wearable sensors. To efficiently integrate multimodal sensor signals, our formulation uses sparse dictionary selection, which minimizes a reconstruction error with a subset (key frames) of the original data. We present experimental results with multimodal datasets captured by wearable sensors in a natural environment. The results suggest multi-sensor information improves the precision of extracted key frames as well as the coverage of an entire video sequence.

We demonstrate how to utilize a two-dimensional image sensor onboard a satellite for determining its attitude. The method is based on image matching between satellite images and Earth surfaces. To achieve this, image feature extraction and robust estimation techniques are employed. Experimental results showed that the accuracy of attitude determination is about 0.02° if the satellite position has been precisely determined. The contribution of this paper is not on technical novelty, as the method has already been described in [1], but on additional examples illustrating the utility of the method.

Re-identification attacks based on a Markov chain model have been widely studied to understand how anonymized traces are linked to users. This approach is known to enable users to be re-identified with high accuracy when an adversary trains a personalized transition matrix for each target user using a large amount of training data, and when all of the anonymized traces are from the target users. In reality, however, the amount of training data for each target user can be very small, since many users disclose only a small amount of their location information to the public. In addition, many of the anonymized traces are from “non-target” users, whose personalized transition matrices cannot be trained in advance. This paper aims to quantify the risk of re-identification in the realistic situation explained earlier. We first utilize the fact that spatial data can form a group structure, and propose group sparsity tensor factorization to effectively train the personalized transition matrices from a small number of training traces. We second formulate a re-identification attack in an “open” scenario, where many of the anonymized traces are from non-target users. Specifically, we regard this type of attack as a biometric verification (or identification) task, and propose a framework and an algorithm for performing this task using a population transition matrix, which is computed from personalized transition matrices. Our experimental results using three real data sets show that a training method using tensor factorization significantly outperforms the maximum likelihood estimation method, and is further improved by incorporating group sparsity regularization.

The noninvasive brain-machine interface (BMI) is anticipated to be an effective tool of communication not only in laboratory settings but also in our daily livings. The direct communication channel created by BMI can assist aging societies and the handicapped and improve human welfare. In this paper we propose and experiment a BMI framework that combines BMI with a robotic house and autonomous robotic wheelchair. Autonomous navigation is achieved by placing waypoints within the house and, from the user side, the user performs BMI to give commands to the house and wheelchair. The waypoint framework can offer essential services to the user with an effectively improved information-transfer rate and is an excellent examples of the fusion of data measured by sensors in the house, which can offer insight into further studies.

We propose a new reconstruction procedure for X-ray computed tomography (CT) based on Bayesian modeling. We utilize the knowledge that the human body is composed of only a limited number of materials whose CT values are roughly known in advance. Although the exact Bayesian inference of our model is intractable, we propose an efficient algorithm based on the variational Bayes technique. Experiments show that the proposed method performs better than the existing methods in severe situations where samples are limited or metal is inserted into the body.

We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones.

Image expansion by linear filtering is attractive and widely used because of its simplicity and efficiency, and many interpolation methods fall in this category. In this study, we model filtering as linear regression from low- to high-resolution color image patches, and propose a learning-based design method of image expansion filters based on sparse Bayesian estimation. Sparseness is imposed on the filter coefficients to obtain compact supports. Image expansion is formulated as the problem of finding the predictive mean of a high-resolution patch given a low-resolution patch to expand. Since an exact evaluation of the predictive distribution is difficult, variational methods are employed to derive an efficient algorithm. Experiments on test data show that good generalization performance is obtained based on sparse filters and that color modeling improves the expansion quality.