Search

Gait-based age estimation is a key technique for many applications. It is well known that age estimation uncertainty is highly dependent on age (i.e., small for children and large for adults), and it is important to know the uncertainty for the above-mentioned applications. Therefore, we propose a method for uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. Specifically, we design a network that takes an appearance-based gait feature as input and outputs discrete label distributions in the integer age domain. We then train the network to minimize a loss function, which is defined as the dissimilarity between the estimated age distribution and the ground-truth age distribution, in addition to the conventional mean absolute error for the estimated age. Additionally, we demonstrate that uncertainty-aware gait-based age estimation is beneficial for two applications: person search by age query and people counting by age group. Experiments on the world’s largest gait database, OULP-Age, demonstrated that the proposed method can successfully represent age estimation uncertainty, and outperforms or is comparable with state-of-the-art methods in terms of age estimation accuracy. Moreover, we demonstrated the effectiveness of the uncertainty-aware framework in applications to person search and people counting through experiments on the database.

Large text corpora are indispensable for natural language processing. However, in various fields such as literature and humanities, many documents to be studied are only scanned to images, but not converted to text data. Optical character recognition (OCR) is a technology to convert scanned document images into text data. However, OCR often misrecognizes characters due to the low quality of the scanned document images, which is a crucial factor that degrades the quality of constructed text corpora. This paper works on corpus construction for historical newspapers. We present a corpus construction method based on a pipeline of image processing, OCR, and filtering. To improve the quality, we further propose to integrate OCR error correction. To this end, we manually construct an OCR error correction dataset in the historical newspaper domain, propose methods to improve a neural OCR correction model and compare various OCR error correction models. We evaluate our corpus construction method on the accuracy of extracting articles of a specific topic to construct a historical newspaper corpus. As a result, our method improves the article extraction F score by 1.7% via OCR error correction comparing to previous work. This verifies the effectiveness of OCR error correction for corpus construction.

Public meeting articles are the key to understanding the history of public opinion and public sphere in Australia. Information extraction from public meeting articles can obtain new insights into Australian history. In this paper, we create an information extraction dataset in the public meeting domain. We manually annotate the date and time, place, purpose, people who requested the meeting, people who convened the meeting, and people who were convened of 1258 public meeting articles. We further present an information extraction system, which formulates information extraction from public meeting articles as a machine reading comprehension task. Experiments indicate that our system can achieve an F1 score of 74.98% for information extraction from public meeting articles.

In this paper, we discuss input/output architectures for convolutional neural network (CNN)-based cross-view gait recognition. For this purpose, we consider two aspects: verification versus identification and the tradeoff between spatial displacements caused by subject difference and view difference. More specifically, we use the Siamese network with a pair of inputs and contrastive loss for verification and a triplet network with a triplet of inputs and triplet ranking loss for identification. The aforementioned CNN architectures are insensitive to spatial displacement, because the difference between a matching pair is calculated at the last layer after passing through the convolution and max pooling layers; hence, they are expected to work relatively well under large view differences. By contrast, because it is better to use the spatial displacement to its best advantage because of the subject difference under small view differences, we also use CNN architectures where the difference between a matching pair is calculated at the input level to make them more sensitive to spatial displacement. We conducted experiments for cross-view gait recognition and confirmed that the proposed architectures outperformed the state-of-the-art benchmarks in accordance with their suitable situations of verification/identification tasks and view differences.

Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC reconstruction algorithm., Comment: 16 pages, 9 figures

We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer’s subjective labels than the readers’. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.

Topic modeling that can automatically assign topics to legal documents is very important in the domain of computational law. The relevance of the modeled topics strongly depends on the legal context they are used in. On the other hand, references to laws and prior cases are key elements for judges to rule on a case. Taken together, these references form a network, whose structure can be analysed with network analysis. However, the content of the referenced documents may not be always accessed. Even in that case, the reference structure itself shows that documents share latent similar characteristics. We propose to use this latent structure to improve topic modeling of law cases using document homophily. In this paper, we explore the use of homophily networks extracted from two types of references: prior cases and statute laws, to enhance topic modeling on legal case documents. We conduct in detail, an analysis on a dataset consisting of rich legal cases, i.e., the COLIEE dataset, to create these networks. The homophily networks consist of nodes for legal cases, and edges with weights for the two families of references between the case nodes. We further propose models to use the edge weights for topic modeling. In particular, we propose a cutting model and a weighting model to improve the relational topic model (RTM). The cutting model uses edges with weights higher than a threshold as document links in RTM; the weighting model uses the edge weights to weight the link probability function in RTM. The weights can be obtained either from the co-citations or from the cosine similarity based on an embedding of the homophily networks. Experiments show that the use of the homophily networks for topic modeling significantly outperforms previous studies, and the weighting model is more effective than the cutting model.

Gait-based age estimation is one of key techniques for many applications (e.g., finding lost children/aged wanders). It is well known that the age estimation uncertainty is highly dependent on ages (i.e., it is generally small for children while is large for adults/the elderly), and it is important to know the uncertainty for the above-mentioned applications. We therefore propose a method of uncertainty-aware gait-based age estimation by introducing a label distribution learning framework. More specifically, we design a network which takes an appearance-based gait feature as an input and outputs discrete label distributions in the integer age domain. Experiments with the world-largest gait database OULP-Age show that the proposed method can successfully represent the uncertainty of age estimation and also outperforms or is comparable to the state-of-the-art methods.

Climate change is one of the most important issues for humanity. To defuse this problem, it is considered necessary to improve energy efficiency, make energy sources cleaner, and reduce energy consumption in urban areas. The Japanese government has recommended an air conditioner setting of 28°C in summer and 20°C in winter since 2005. The aim of this setting is to save energy by keeping room temperatures constant. However, it is unclear whether this is an appropriate temperature for workers and students. This study examined whether thermal environments influence task performance over time. To examine whether the relationship between task performance and thermal environments influences the psychological states of participants, we recorded their subjective rating of mental workload along with their working memory score, electroencephalogram (EEG), heart rate variability, skin conductance level (SCL), and tympanum temperature during the task and compared the results among different conditions. In this experiment, participants were asked to read some texts and answer questions related to those texts. Room temperature (18, 22, 25, or 29°C) and humidity (50%) were manipulated during the task and participants performed the task at these temperatures. The results of this study showed that the temporal cost of task and theta power of EEG, which is an index for concentration, decreased over time. However, subjective mental workload increased with time. Moreover, the low frequency to high frequency ratio and SCL increased with time and heat (25 and 29°C). These results suggest that mental workload, especially implicit mental workload, increases in warmer environments, even if learning efficiency is facilitated. This study indicates integrated evidence for relationships among task performance, psychological state, and thermal environment by analyzing behavioral, subjective, and physiological indexes multidirectionally.

Drowsiness is a major factor that hinders learning. To improve learning efficiency, it is important to understand students' physical status such as wakefulness during online coursework. In this study, we have proposed a drowsiness estimation method based on learners' head and facial movements while viewing video lectures. To examine the effectiveness of head and facial movements in drowsiness estimation, we collected learner video data recorded during e-learning and applied a deep learning approach under the following conditions: (a) using only facial movement data, (b) using only head movement data, and (c) using both facial and head movement data. We achieved an average F1-macro score of 0.74 in personalized models for detecting learner drowsiness using both facial and head movement data.

Ascertaining the level of reading comprehension in a learner is often a challenging task. Although written tests and self-evaluations can provide feedback as to whether an individual understands a particular topic, they are not real time, do not necessarily provide a full picture of the reader’s comprehension, and can be subjective.In this paper, we present initial results of a study to determine better ways to evaluate a user’s comprehension and understanding of educational comic books using pupillometry. Our system recreates the reading experience of an immunology comic book in virtual reality (VR), allows users to rate their comprehension of a particular section, and records eye data during the learning task. Through experiments, we explore the potential of this interface to facilitate learning and examine pupil metrics that might be used to automatically classify comprehension and understanding at the category (topic) level. We also discuss numerous design considerations that should be taken into account when designing future interfaces for evaluation of learning or comprehension.