Reviewerly/exHarmony huggingface dataset:
An investigation on the methods of noise countermeasures for safeguarding intangible cultural property.- Attempt of noise compatibility planning rooted in the local community-

In the Ogino area of Atsugi City, Kanagawa Prefecture, Wadaiko(Japanese drums) is practiced and passed down to generations for performance at festivals and other events. However, nearby residents would sometimes make noise complaints to the government about the sound generated. Therefore, in Atsugi City, the use of city facilities is prohibited when the main purpose is to practice Wadaiko, creating a situation in which practices are restricted to only some locations. Physical noise control for membranophone instruments such as Wadaiko is difficult from the architectural perspective, as well as the cost perspective if it were to be carried out by the local government. Therefore, the final goal of this study is to find a solution to the noise problem through social measures so that traditional musical instruments whose physical noise control is difficult, such as Wadaiko, will continue to be passed down to generations. First, we uncovered the appearance of the acoustic characteristics of Wadaiko through acoustic measurements. Additionally, by working with the locals, we obtained a clear picture of the current noise complaints and situations. Next, as part of the Noise Compatibility Planning attempt, we set up a website and constructed an operational system to collect feedback from the locals regarding the Wadaiko sounds in order to cater to all lifestyles and circumstances. This paper covers a part of the initial stage of the project. In consideration of people who are easily affected by noise, such as examinees, sick patients, those who are hypersensitive to hearing, and those of specific lifestyles, the community association and the university collaborated to build a website accessible to all residents in the area. Using the website, they schedule practice days and times and create and operate a noise map by monitoring environmental noise regularly. Although not everyone can escape from the Wadaiko noise, we have focused on noise control measures that considered the characteristics of every individual, which is possible only in a small community.
Clustering to Support Users Finding Unexpected Perspectives in Brainstorming

We propose a clustering method to support users in discovering unexpected perspectives of ideas. The proposed method performs principal component analysis on a set of ideas and clusters ideas with unexpected perspectives by reducing specific dimensions. By presenting the clustered results to users, the proposed method encourages discovery of unexpected perspectives. We conducted user experiments to verify the effectiveness of the proposed method, where participants were asked to name the groups of ideas output by the proposed method. We then asked the participants to evaluate the unexpectedness of the group names. The results suggest that the proposed method can cluster a set of ideas from unexpected perspectives compared to existing clustering methods.
Cost-Driven Selective Information Control for Multi-Layered Neural Networks

The present paper tries to propose a new type of information-theoretic method to control selective information and its corresponding cost. The method aims to restructure connection weights, depending on input or output information. In addition, partially compressed weights are proposed to examine how input or output information is transmitted in multi-layered neural networks. The method was applied to the web marketing data set. The results show that the selective information could extract information focused on outputs, and the cost reduction was related to the acquisition of input information with its gradual reduction in multiple hidden layers. The results show a possibility that internal representations can be flexibly changed by focusing on input or output information.
Case Study of Proficiency-Based Class Composition Using Machine Learning

This paper reports a case study of creating a prediction model of English placement test scores for proficiency-based English language classes for incoming first-year students. Since the English placement test could not be administered in FY2020 due to the COVID-19 pandemic, we used data from the past five years to create a random forest regression model was created using data from the past five years. Since the variables that could be used were limited to fixed information at the time of admission, the number of variables was minor, such as the type of entrance examination and high school types. Although the performance metrics of the model were not perfect, it was judged to be practical for using the prediction results, namely, class placement. In the following year, 2021, the English placement test could be administered, so the prediction model created in 2020 was used to verify the prediction results and actual performance. As a result, it was confirmed that the performance evaluation index tended to decrease. However, the performance was judged to be within the practical range, and the generalization performance of the forecasting model was confirmed. In addition, when the importance of the variables used in the prediction model was checked, the importance of the recommended entrance examination system was high. This suggests that the difference in English proficiency at the time of entrance is mainly due to the entrance examination system and the future that devise a countermeasure is necessary such as remedial education.
Identification of Nighttime Vehicle Images

To identify vehicle models from images taken by security cameras and drive recorders is one of the important methods for investigations. However, it is not easy to identify the vehicle models from the images at night when crimes often occur. On the other hand, recently, LED lamps such as front lights and side marker lights have become popular. The shapes of LED lamps have become more unique, increasing the possibility of identifying vehicle models at night using the features of LED lamps. Therefore, in this paper, we estimate the identification accuracy of vehicle models by deep learning focusing on night images. Two drive recorders were attached to the front and back of a car to capture images of the front and rear of oncoming vehicles. We manually gave model names to some of the images and created a dataset of 285 cars, which is equivalent to about 70&#x0025; of the domestic cars released in Japan after 2001. The dataset consisted of 40 images per one model, that is 10 daytime front images, 10 daytime rear images, 10 nighttime front images, and 10 nighttime rear images. To estimate the identification accuracy of vehicle models, transfer learning on PyTorch was performed using CNN models such as ResNeXt101&#x005F;32x8d. The results of evaluation experiments showed that the accuracy of the nighttime images is lower than that of the daytime images, and this tendency is stronger in the front images. Therefore, it is useful to use the rear images to identify vehicle models at night.
Performance Evaluation of Volunteer Computing-based Neural Network on Ethereum Platform

In recent years, the application of distributed computing has become more and more widespread. Volunteer computing has gained people&#x0027;s favor in terms of only requiring low cost to exchange for a large amount of computing power. Volunteer computing is defined as a kind of distributed computing infrastructure with a large number of computers voluntarily provided by the public. The integrated computing and storage resources are collected and processed by a central server. However, for volunteer computing, security issues are of paramount importance due to the computing source provided by the volunteers. The attack on any computer in the volunteer network may have an impact on the whole network. On the other hand, blockchain is a chain storage structure that has been widely used in recent years, and non-tamperability is one of its most important characteristics. Therefore, this research aims to improve the security and credibility of volunteer computing by using blockchain technology. Accordingly, a novel volunteer network is established on the Ethereum test network named Ropsten. Its performance and consumption are evaluated with a neural network used for computing. The results demonstrate that proper security properties of volunteer computing can be guaranteed by the proposed blockchain-based approach, although a comparatively low time cost and commission consumption is required.
Synchronous Dropout for Convolutional Neural Network

In this paper, we propose a synchronous dropout that improves convolutional neural network (SD-CNN). The dropout is famous normalization technique for the artificial neural network. In the standard dropout, the neurons are randomly chosen with a fixed probability. In our method, we decompose the neurons in hidden layer to some groups. The neurons in hidden layer are randomly chosen by neuron groups, and all neurons in chosen group, are skipped a training phase with iteration. The group dropout increases training normalization than the standard dropout because the number of chosen neurons is constant. We compare that the group dropout with the standard dropout by using Fashion-MNIST.
Immersive SciFi Prototyping for Responsible Innovation - A humanistic Approach to Technological Change

Assessing the consequences of emerging technological concepts on human existence is a challenge that requires unconventional approaches. Science Fiction Prototyping is a methodology that builds on the art of telling stories about our future. Since 2017, we have been using Science Fiction Prototyping to develop an extended approach we call immersive SciFi Prototyping. Immersive SciFi Prototyping features multilinear Virtual Reality (VR) scenarios in order to promote the exchange between scientists and the broad public on the consequences of Emerging Technologies. The resulting discourse will bring together scientists, decision makers and laypeople who can discuss opportunities and risks of Emerging Technologies based on their immersive experiences.
Neural Conversation with Memory Mechanism

We propose a neural conversation system with memory mechanism to realize natural conversation exchanges considering the previous utterances. The neural conversation system consists of a Sequence-to-Sequence model and a memory mechanism. The Sequence-to-Sequence model can generate gramatically correct replies and the memory network can consider the previous utterances. The proposed method is trained with Cornell Movie-Dialog corpus and realize conversations between human and a; computer. We confirm that the proposed method can generate replies depending on the previous utterances but it is difficult to generate semantically correct utterances.
Leaf-Preserving Distance between Rooted Labeled Caterpillars

In this paper, we introduce a leaf-preserving distance between rooted labeled trees (trees, for short) as a distance that a leaf in a tree is corresponding to a leaf in another tree. Then, we show that the leaf-preserving distance is always smaller than or equal to the bottom-up distance and incomparable with the alignment distance, the isolated-subtree distance and the segmental distance, but the problem of computing the leaf-preserving distance between trees is MAX SNP-hard. On the other hand, for a rooted labeled caterpillar (caterpillar, for short) that is a tree transformed to a rooted path after removing all the leaves in it, we design an algorithm to compute the leaf-preserving distance between caterpillars in O(h<sup>2</sup>&#x03BB;) time, where h is the maximum height and &#x03BB; is the maximum number of leaves for given two caterpillars. Finally, we give experimental results for computing the leaf-preserving distance for caterpillars.
A guide for inexperienced players of basketball to master basic field of vision

This paper describes a methodology of training conscious view for players of basketball. In order for this purpose, we designed and implemented a learning support system arranged from original 2D-simulator. The system provides bird&#x0027;s-eye-view of the court where each defense in the simulator has the field guide of view angle. Through an experimental use, our system brought positive effect in learning basic view of players taking a role in defense.
Multiple Domains Knowledge Graph Search via Heuristic Algorithm for Answering Complex Questions

Question answering (QA) system is widely used because it finds the information that users want. Users can ask a variety of questions to the QA system, and the type of question is divided into a simple question and a complex question. A simple question is one query with one question while a complex question is one query with two or more questions. Although many studies have greatly improved the ability of the QA system to answer simple questions, answering complex questions remains a difficult problem. To solve the problem, we use the information from multiple domains and propose a method to quickly retrieve information from many data through a heuristic algorithm. For experiments, we use the movie, person, and country domain data as a knowledge base for the QA system, which is able to help solve the complex question. Compared to the widely used graph search algorithm Breadth-First Search (BFS) and Depth-First Search (DFS), the proposed method reduces the search time by 62&#x0025; and 44&#x0025; for finding the answers, respectively.
A Comparative Study of Japanese Input Speed by Keyboard of Elementary School Children Learning with 1:1 Devices for the First Time

This study involved a four-month investigation of Japanese input skills by keyboard of elementary school children learning with 1:1 devices for the first time. Participants were from two classes of elementary schools. The X class was 4th grade, and the Y class was 6th grade. We divided the survey results into two groups based on input speed by keyboard. Results of the study indicate that the input speed was significantly improved in both input speed groups after four months of practice and utilization. In the upper group, 6th grade significantly exceeded 4th grade during second and fourth months of the study. However, in the lower group, the 6th grade was significantly lower than the 4th grade in third and fourth months. Therefore, it is necessary to pay particular attention to supporting the acquisition of Japanese input skills by keyboard, especially for children in the lower group.
Document Selection for Transfer Learning in Abstractive Summarization

Transfer learning has attracted attention as a promising approach to compensate for a lack of training data in a target domain. However, methods of transfer learning for document summarization tasks have not been explored, and its effect on performance is still unclear, although the summarization task likely suffers a shortage of training data due to the high cost to prepare summaries. Particularly, the negative transfer effect that deteriorates the performance is concerned when we use a set of training documents that are irrelevant to the target summarization domain. In this paper, we propose a method that selects training documents from the source domain that are expected to be useful for the target summarization. The proposed method is based on the similarity of distributions between each source document and a set of target documents. Though the experiment, we confirmed that (i) the negative transfer potentially happens also in the document summarization tasks, and (ii) the proposed method effectively works to avoid the issue and to improve the performance of summarization.
Person Name Extraction from TV program Using Pre-trained Language Model and News Headline

In this study, we focus on extracting person names from text contained in from TV programs and TV-CMs broadcast in Japan. Person name extraction from TV programs is a challenging task because person names are often recognized with other entity names such as locations and organizations. To tackle this problem, we experiment with an existing named entity extraction method using a list of names extracted from Wikipedia. However, this method results in low precision in extracting the person names, which leads to the problem that the list of person names is not effective for TV program texts. In this paper, to solve this problem, we propose a person name extraction method using Conditional Random Fields (CRF) with ELMo pre-training language model. Also, we propose to use news headlines to construct the person name extraction model for effective extraction of person names performing on the TV programs. As a result of our experiments, the proposed model trained with training data and news headlines provides the highest precision. These results show that adding the news headlines as external information to TV program data is effective for person name extraction.
Analysis of the Actual State of Learning through Career Education as First-Year Experience Using a Topic Model

A topic model was used to analyze what kind of learning was experienced by many first-year students who took a career development class. This study targeted a class offered as a compulsory subject at a regional university in Japan. For the analysis, we used free descriptions from 2,652 students who took classes from 2011 to 2019. The percentage of topics included in the free descriptions was calculated, and the yearly changes in each topic were examined. The results showed that the percentage of topics such as &#x201C;self-image clarification and communication skills,&#x201D; &#x201C;diverse experiences,&#x201D; and &#x201C;self-efficacy&#x201D; changed insignificantly, indicating that these topics were robust in learning.
Indirect Matching Using Dynamic Cues for Effective Similar Shape Retrieval

Shape images such as vector graphics have become popular for creating various types of online content. The importance of similarity search systems for shape images has been increasing. As a fast similarity retrieval method, indirect matching has been proposed. The indirect matching quickly estimates the similarity between each shape in the database and the query using a small number of cue images. The retrieval accuracy of the indirect matching, however, significantly changes depending on the combination of cue images. In addition, we have found the best combination of cue images is dependent on the query. To improve the retrieval accuracy of the indirect matching, we propose a dynamic selection method to determine optimal cue images depending on a given query in real time. Experimental results have shown that retrieval accuracy and performance can be improved by the proposed method.
Recommender System for Familiarizing Gradually Unfamiliar Domains

In this paper, we propose a method for recommending a sequence of items to users to gradually familiarize them with a field they are not familiar with but want to learn. In the proposed method, we create a graph structure in which nodes correspond to items and edge weights correspond to similarities between items. Next, based on the graph structure, the proposed method provides the users with a series of items to familiarize them with the target field step-by-step in the shortest possible time. In this paper, we use &#x201C;music&#x201D; as an example domain and conduct a questionnaire survey to verify whether the proposed method enables users to familiarize themselves with unfamiliar domains.
Discovering outliers in the Mars Express thermal power consumption patterns

The Mars Express (MEX) spacecraft has been orbiting Mars since 2004. The operators need to constantly monitor its behavior and handle sporadic deviations (outliers) from the expected patterns of measurements of quantities that the satellite is sending to Earth. In this paper, we analyze the patterns of the electrical power consumption of MEX’s thermal subsystem, that maintains the spacecraft’s temperature at the desired level. The consumption is not constant, but should be roughly periodic in the short term, with the period that corresponds to one orbit around Mars. By using long short-term memory neural networks, we show that the consumption pattern is more irregular than expected, and successfully detect such irregularities, opening possibility for automatic outlier detection on MEX in the future.
Web Scraping Methods on Odoo Framework to Collect Rupiah Exchange Rate From Bank Indonesia Website

The foreign currency exchange rate against the rupiah in the Odoo ERP framework system accommodates multi-currencies. However, Odoo has not updated the rupiah exchange rate based on the value from Bank Indonesia regularly. The exchange rate is the price at which one country&#x0027;s currency can be exchanged for another country&#x0027;s currency. The rupiah exchange rate in business has an influence on the cost revenue as well as the profit of the Indonesian company. The problem is updating the rupiah exchange rate must be done manually by looking at the exchange rate on the Bank Indonesia website and entering it into the ERP system according to the current date. The Rupiah exchange rate in foreign currencies needs to be updated regularly by Bank Indonesia exchange rates. To collect rupiah exchange rate uses the Odoo Framework ERP system using the web scraping methods to extract data from Bank Indonesia exchange rates, the library used in scraping is Beautiful Soup. Beautiful Soup is a Python package for parsing HTML and XML. Is used to extract data from HTML, which is useful for web scraping. Rupiah exchange rate updates will be scheduled by the system every day at 12.30 p.m. GMT+07.
Multi-turn dialogue generation considering speech information of each speaker by adding recurrent neural networks

A dialogue generation method using neural networks (NNs) has been proposed. The HRED model is a model for multi-turn dialogues by creating a hierarchical structure by layering several encoder-decoder models. Furthermore, the VHRED model generates a variety of responses by adding latent variables. However, since these models do not consider the user who has spoken, they generate inconsistent responses in the same dialogue, which is a problem. In this study, instead of using the user&#x0027;s embedding vector, we add a user recurrent NN (User-RNN) to retain the speech information of each speaker and generate consistent responses.
Tourism Evaluation of Zones for Foreigners Visiting Himeji City Using Support Vector Machines

In this paper, a method of evaluating zones from the viewpoint of sight scene resources in Himeji City, Japan, is presented to promote the tourism of that city for foreign tourists. It employs support vector machine (SVM for short). Data presented to discrimination models constructed by SVM learning are prepared from numbers of tourists totaled by country. The element value of the data is therefore equal to the number of the tourists coming from some country and visiting some zone of one square kilometer in Himeji City. The discrimination model judges whether a zone is worth to visit for the tourists coming from each of the following countries: France, United Kingdom, Germany, Spain, Singapore, Australia, and United States of America. Experimental results reveal that the proposed method achieves favorable values of recall, precision, and F-measure on average when training data are prepared from numbers of the tourists coming from six countries out of the above seven ones considered to be of importance for the promotion.