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Estimating Congestion in a Fixed-Route Bus by Using BLE Signals
No full textInformation on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%)journal articl
Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs
No full textBackground:Patients with breast cancer have a variety of worries and need multifaceted information support. Their accumulated posts on social media contain rich descriptions of their daily worries concerning issues such as treatment, family, and finances. It is important to identify these issues to help patients with breast cancer to resolve their worries and obtain reliable information.Objective:This study aimed to extract and classify multiple worries from text generated by patients with breast cancer using Bidirectional Encoder Representations From Transformers (BERT), a context-aware natural language processing model.Methods:A total of 2272 blog posts by patients with breast cancer in Japan were collected. Five worry labels, “treatment,” “physical,” “psychological,” “work/financial,” and “family/friends,” were defined and assigned to each post. Multiple labels were allowed. To assess the label criteria, 50 blog posts were randomly selected and annotated by two researchers with medical knowledge. After the interannotator agreement had been assessed by means of Cohen kappa, one researcher annotated all the blogs. A multilabel classifier that simultaneously predicts five worries in a text was developed using BERT. This classifier was fine-tuned by using the posts as input and adding a classification layer to the pretrained BERT. The performance was evaluated for precision using the average of 5-fold cross-validation results.Results:Among the blog posts, 477 included “treatment,” 1138 included “physical,” 673 included “psychological,” 312 included “work/financial,” and 283 included “family/friends.” The interannotator agreement values were 0.67 for “treatment,” 0.76 for “physical,” 0.56 for “psychological,” 0.73 for “work/financial,” and 0.73 for “family/friends,” indicating a high degree of agreement. Among all blog posts, 544 contained no label, 892 contained one label, and 836 contained multiple labels. It was found that the worries varied from user to user, and the worries posted by the same user changed over time. The model performed well, though prediction performance differed for each label. The values of precision were 0.59 for “treatment,” 0.82 for “physical,” 0.64 for “psychological,” 0.67 for “work/financial,” and 0.58 for “family/friends.” The higher the interannotator agreement and the greater the number of posts, the higher the precision tended to be.Conclusions:This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients’ worries and give them timely social support.journal articl
Towards Morphological And Syntactic Analyses For The Khmer Language
Get PDF奈良先端科学技術大学院大学博士(工学)doctoral thesi
Solar Irradiance Forecasting using Total Sky Images for Maximum Power Point Tracking
Get PDF奈良先端科学技術大学院大学修士(工学)master thesi
Evaluation of Cross-modality Image Synthesis and Bayesian Active Learning in Segmentation of Lower Limb Muscles in MR Images
Get PDF奈良先端科学技術大学院大学修士 (工学)master thesi
A Rendering Method of Microdisplay Image to Expand Pupil Movable Region without Artifacts for Lenslet Array Near-Eye Displays
Get PDFNear-eye displays (NEDs) with lenslet array (LA) are a technological advancement that generates a virtual image in the observer's field of view (FOV). Although this technology is useful for designing lightweight NEDs, undesirable artifacts (i.e., cross-talk) occur when the user's pupil becomes larger than the pupil practical movable region (PPMR) or moves out of the PPMR. We proposed a rendering method for microdisplay images that takes pupil size into account and included the idea of pupil margin in the ray tracing process. Ray lights emitted by one microdisplay pixel (MP) enter the pupil and pupil margin area after passing through a number of lenses. Each lens at the MP corresponds to one virtual pixel (VP) on the virtual image plane. The weight of each VP is the intersection area between the ray light column and the pupil and pupil margin divided by the sum of intersecting spaces between all the ray light columns generated by the MP and the pupil and pupil margin. The value of each MP is determined by the number of VPs and the related weight. Through retina image simulation studies, we confirmed that the proposed rendering approach substantially enlarges PPMR to accommodate large pupil diameters and wide transition distances while reducing eye relief to an optimal (sunglasses) distance.conference pape
Weakly Byzantine Gathering with a Strong Team
Get PDFWe study the gathering problem requiring a team of mobile agents to gather at a single node in arbitrary networks. The team consists of k agents with unique identifiers (IDs), and f of them are weakly Byzantine agents, which behave arbitrarily except falsifying their identifiers. The agents move in synchronous rounds and cannot leave any information on nodes. If the number of nodes n is given to agents, the existing fastest algorithm tolerates any number of weakly Byzantine agents and achieves gathering with simultaneous termination in O(n400B7X(n)) rounds, where |Λgood| is the length of the maximum ID of non-Byzantine agents and X(n) is the number of rounds required to explore any network composed of n nodes. In this paper, we ask the question of whether we can reduce the time complexity if we have a strong team, i.e., a team with a few Byzantine agents, because not so many agents are subject to faults in practice. We give a positive answer to this question by proposing two algorithms in the case where at least 4f2+9f+4 agents exist. Both the algorithms assume that the upper bound N of n is given to agents. The first algorithm achieves gathering with non-simultaneous termination in O((f+|&Lambdagood|)00B7X(N)) rounds. The second algorithm achieves gathering with simultaneous termination in O((f+|&Lambdaall|)00B7X(N)) rounds, where |&Lambdaall| is the length of the maximum ID of all agents. The second algorithm significantly reduces the time complexity compared to the existing one if n is given to agents and |&Lambdaall|=O(|&Lambdagood|) holds.journal articl
