Reviewerly/exHarmony huggingface dataset:
Classifying Building and Ground Relationships Using Unsupervised Graph-Level Representation Learning

When designing, architects must always consider the ground on which their buildings are supported. Our aim is to use data mining and artificial intelligence (AI) techniques to help architects identify emerging patterns and trends in building design and suggest relevant precedents.
Revisiting Convolutional Neural Networks from the Viewpoint of Kernel-Based Methods

Convolutional neural networks, as most artificial neural networks, are frequently viewed as methods different in essence from kernel-based methods. In this work we translate several classical convolutional neural networks into kernel-based counterparts. Each kernel-based counterpart is a statistical model called a convolutional kernel network with parameters that can be learned from data. We provide an alternating minimization algorithm with mini-batch sampling and implicit partial differentiation to learn from data the parameters of each convolutional kernel network. We also show how to obtain inexact derivatives with respect to the parameters using an algorithm based on two inter-twined Newton iterations. The models and the algorithms are illustrated on benchmark datasets in image classification. We find that the convolutional neural networks and their kernel counterparts often perform similarly. Supplemental appendices and code for the article are available online.
High-Performance FPGA Embedded System for Deep learning-based Thermal Object Tracking

Recently, Field programmable gate array (FPGA) has been adopted for accelerating the implementation of deep learning networks due to their ability to maximize parallelism as well as their energy efficiency. In this paper, we address the usage of convolution neural networks for single tracking in thermal infrared images (TIR) on FPGA embedded systems. Based on the previous tracking network named SiamFC++, we build a tracker for thermal infrared single object tracking problem by utilizing several TIR public datasets. Then we deploy and optimize SiamFC++-TIR tracker models on Xilinx FPGA platform with Vitis AI development environment. Experimental results show that our TIR tracker achieves outstanding performance in two public TIR tracking benchmarks. In addition, we construct a video dataset collected from our thermal infrared device, named QDT-TIR, with annotation of various thermal object classes, scenarios, and challenges. To the best of our knowledge, our tracker is the first TIR tracker that achieves a high operating speed of 102 FPS and low power consumption of 9.744 Watt on FPGA with a precision score of 0.681 on LSOTB-TIR benchmark.
Random Fitness-Diversity Weight-based Hummingbird Optimization Algorithm

Optimization problems are widespread in engineering, physics, economics, biology, etc. With the deepening of research in various fields, optimization problems have become more complex. Evolutionary algorithm is an effective solution for the complex optimization problem. In this study, we propose a novel evolutionary algorithm, namely RHO, which adopts the search strategy that simulates the foraging path of hummingbirds and random fitness-diversity weight. It balances two sets of critical factors in EA, i.e, exploitation and exploration, convergence and diversity. Experimental results on numerical optimization problem set CEC2017 show that RHO has the dominant advantage over other state-of-the-art evolutionary algorithm-based optimization methods.
Free handwritten string recognition using CNN and DP matching

While handwritten zip code recognition and ledger sheet recognition are in practical use, character recognition technology for free handwritten documents is still in the process of commercialization. If character recognition for free handwritten documents is realized, scanned images of notes and memos written on paper can be converted into text data on a computer, which will be useful for keyword searches and natural language processing. One of the reasons why character recognition for free handwritten documents is still in the research stage is that it is difficult to detect lines and extract characters from a document. In order to improve the accuracy of character recognition for free handwritten documents, it is first necessary to improve the accuracy of line detection and character segmentation. In this study, we propose a method to segment characters from words or strings using CNN and dynamic programming so that the sum of character similarities is optimal, and aim to improve the accuracy of character segmentation
Quaternion Convolutional Neural Networks for Depth Estimation

Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images [1]. Recently, Depth Estimation is interesting to global AI researchers. We can see applications of deep learning in solutions that. But deep learning methods before only research solutions in real-valued neural networks. In this paper, we want to propose a method, which is a quaternion-valued convolutional neural network because quaternion operations are great for depth processing and complex images, so neural networks that use quaternion-valued will have better than neural networks that use real-valued.
Readmission Prediction for Heart Failure Patients Using Features Extracted From SS-MIX

Increased demand for healthcare has resulted in necessity of utilizing electronic medical record. Standardized Structured Medical Information eXchange (SS-MIX) is a standard data storage format used in Japan to share clinical data. SS-MIX contains standard medical receipt data which is automatically stored. In this paper, we utilize the SS-MIX data to predict readmission of patients, who are suffering from heart failure. (i.e., We define the readmission prediction task as a binary prediction, where whether a patient readmits within N days after hospital discharge or not.) We train classifiers based on features (e.g., disease history, allergy, prescription, etc.) that are extracted from medical receipt data which is contained in SS-MIX. The experimental result showed that above features are effective for the task of readmission prediction; in particular, LightGBM outperformed other classifiers. Moreover, features from ICD10 codes are shown to be effective to predict readmission.
Design of Federated Learning Engagement Method for Autonomous Vehicle Privacy Protection

An autonomous car is defined as a vehicle that can be operated only by an autonomous driving system and does not require a driver or intervention of a driver or passenger. Various types of data collected for fully autonomous car operation include personal information such as location, image, physical, behavior information, and license number. It is hard for a driver or pedestrian to recognize or control automated collection and processing of personal information by autonomous vehicles in real time. It is, therefore, necessary to prepare and apply appropriate countermeasures such as minimizing the collection of personal information, deleting and encrypting unnecessary information, etc. Federated Learning is a distributed machine learning technique that can learn Al models in cooperation with each other without directly sharing data distributed and stored in multiple locations, such as devices or institutions. Instead of transmitting individual client data to the central server (cloud), the Al model of the central server is sent to the client to train the model with each data. By repeating this process, the global Al model of the central server becomes more general and the accuracy of the local Al model of the client improves. In order to protect personal information collected while driving an autonomous car, this paper proposed a federated learning engagement method, where cross-device was used to engage in the federated learning consisting of a very large number of mobile devices, and based on this, personal information can be protected for the efficient operation of autonomous vehicles.
Synergistic Effect of Adaptive Synapse Arrangement and Column-based Decoder in Cortical Learning Algorithm

The cortical learning algorithm (CLA) is a time-series forecast method designed based on the human neocortex. CLA has memory components named columns. A column synapse links a column to an input data bit. CLA encodes real value input data in each time step into a discrete representation based on column synapse relationships. CLA also represents the data incoming next time step as a discrete representation. A column-based decoder decodes the discrete representation into a real value based on column synapse relationships. Therefore, column synapse relationships have a large impact on forecast accuracy. In this work, we propose a CLA introducing an adaptive column synapse arrangement that improves the column synapse relationships for the column-based decoder. We showed that the adaptive column synapse arrangement boosts the forecast accuracy improvement of the column-based decoder on artificial time-series data and real-world electricity load forecast.
An Interactive Learning Platform with Machine Translation for Practicing Text-Based Conversational English

Text-based conversation, such as chatting or texting, has become an essential means of written communication. This paper investigates the importance of practicing textual conversations for foreign language learners, focusing on Ll Japanese speakers learning English as a foreign language. We proposed a web-based interactive platform that utilizes neural machine translation (NMT) to support language learners practice text-based conversational English. Our proposed system enables the learners to practice texting with other users and conduct exercises for self-practice and assessments. The platform provides immediate feedback required by learners and also allows teachers to verify the assessment result, implementing the human-in-the-loop framework. The verified feedback from the teachers can then be used to improve the evaluation translation, especially for NMT models using metrics, such as BLEU, that depend on reference translations to calculate the scores.
A method for estimating the volume of clusters built by Growing Neural Gas

3D space perception is playing an important role in autonomous robots completing a task adaptively in the form of detecting target objects and estimating the 3D pose of target objects. This paper utilizes a growing neural gas (GNG) based method called GNG with different topologies (GNG-DT) for reconstructing unstructured point clouds. Next, for extracting a feature from clustering results, we propose a GNG based volume estimation method. Finally, we display a sequence of experimental results of the proposed method using simulation data sets and 3D point cloud datasets to evaluate the proposed method and discuss its effectiveness.
Building a Control Method through Verbalizing the Internal Behavior of a Deep Reinforcement Learning Model

Due to its black-box nature, interpreting the internal behavior of a deep reinforcement learning model is a challenging task for humans. We therefore apply fuzzy modeling for the input-output relationships of a learned deep reinforcement learning model and express these relationships with fuzzy linguistic variables to make linguistic control rules. In this study, we use CartPole as an experiment subject; explain control rules of the model learned by a Deep Q-Network (DQN) with fuzzy linguistic variables; and attempt to control the CartPole using those control rules. We use two approaches to construct control rules. One is to construct control rules from input data and the other is to construct control rules from output data. As a result, through experiments, we have confirmed the trade-off between control performance and the number of rules. The learned DQN of CartPole model stands 200 steps, whereas the fuzzy controllers constructed in this study stand 171.9 steps on average. Though the accuracy of controling CartPole by constructed fuzzy controllers is lower than DQN, it is good that the internal behavior can be understood through the constucted rules.
Experiment to Investigate Awareness of Tastes for Users in Interactive Evolutionary Computation

Interactive evolutionary computation (IEC) is one type of evolutionary computation which includes a human user as an evaluation system. Unlike a computer program in standard EC, an IEC user is expected to be aware of his/her tastes more clearly with iteration of evaluations. However, there has not been any research that tries to confirm it so far. If it is confirmed, IEC is desired to design based on that fact. In the paper we first propose an experimental way to investigate it and then conduct an experiment following the proposed way. In addition, we try to extract features of solutions which correspond to the awareness well. The results show that most subjects can be more aware of their tastes with iteration and the extracted features correspond to the awareness well.
Viewing Distance Estimation in Eye Tracking Based on Self-Point Estimation on Map

This paper presents a method to estimate viewing distance from the self-point to the fixation point in eye tracking. This method is designed for outdoor experiments of human visual attention using an eye tracker. This method uses positions on a map and viewing angles of the eye tracking. We performed an experiment in which participants wearing an eye tracker looked at a pole 19.9 m distant and this viewing distance was estimated by the proposed method. Standard deviation of the relative error was less than 1.6 percent and its mean was less than 0.8 percent.
Transfer learning considering the impact of data augmentation on each layer of the source model

Deep learning (DL) technology using neural networks that reproduce the behavior of human neurons has gained significant traction in various fields. However, the large amount of data required in DL is difficult to obtain and prepare. To address this shortage, data augmentation is used to pseudo-increase the amount of data through targeted transformations. Generally, data augmentation is evaluated based on the accuracy of the model created using data augmentation in relation to the data for validation; the impact on each layer of the model is largely ignored. In this study, the impact of data augmentation on each layer of the model is considered. For this purpose, fusion transfer learning is performed using multiple augmentation methods from the same source.
Application of On-Road Surveillance Cameras for Traffic Light Optimization

In this work, we propose an application of on-road surveillance cameras for traffic light optimization. The experiments consist of two parts one is data collection and another is traffic light optimization. In the data collection part, data were collected from the main road with one conjunction. Vehicles and Pedestrians were detected using the Yolov3 method. For lane detection, the Hough transform was used. After collecting the data traffic lights were optimized by some rules. Moreover, due to lack of data and sensitivity, the Simulation of Urban MObility (SUMO) simulator was also used for experiments. We achieved traffic light optimization by 15 seconds in real-time experiments while in the simulator, the time difference between the normal and optimized situation was 129 seconds to clear all the vehicles.
Application of open data and integer linear programming to real-time generation of one-stroke traveling route for collecting and distributing for food loss reduction

We believe that alleviating poverty and reducing food loss can contribute to the SDGs [1], and we have designed PoC for real time generation of one-stroke routes for real-time collection and distribution of food loss. You can easily create routes in real time using open data and integer programming.
Traffic Landmark Quality Evaluation Using Efficient VGG-16 model

Deep learning technologies have developed rapidly in the last few years. Ever since the development of highly efficient GPUs, the deep learning community managed to put together a variety of neural network models, which addressed a wide range of problems. Of these models, we find VGG-16 [1], which is a classical model for solving computer vision challenges. On the other hand, as cities around the world grow bigger, the need for sustainable infrastructure is crucial to guarantee the efficiency of these systems, and in some cases, the safety of the citizens. In this spirit, the local government facilities of Mie prefecture in Japan, alongside Mie university, initiated a project to build a camera-based monitoring system. This system aims to keep track of the rate of deterioration of traffic landmarks, which is essential for road safety and sustainability. The development of such technology can also be of benefit to the self-driving car industry. Subsequently, This area of research has not been explored as effectively as other areas in the field of computer vision. In this work, we developed a solution to achieve traffic landmark quality evaluation. We trained a new model, which is a slightly different version of VGG-16 models, and which we will refer to as ”Efficient VGG-16”. We used a dataset of images provided by the local government facilities of Mie. We also relied on the results of a segmentation model, which we developed in a previous work [2]. using this segmentation model, we obtained binary masks denoting traffic landmarks, by performing inference on 800 images. We then used the binary masks and the labels for quality, to train an ”Efficient VGG-16” model alongside VGG-16 and Res-Net-18 [3] models. Our model succeeded at performing traffic landmark detection with an MSE = 3.62% and showed better performance than the original VGG16 in several metrics.
A Path Planning Method Based on Theta-star Search for Non-Holonomic Robots

This study presents an improved path planning method for non-holonomic robots based on the Theta-star search. Theta-star search produces any-angle waypoints as long as each pair of waypoints satisfies line-of-sight constrain in grids with blocked and unblocked cells. However, the waypoints of Theta-star search are usually selected around obstacles/walls’ edges, causing the robot to drive at a minimal collision-free distance from obstacles/walls. Therefore, we propose a new evaluation function that considers surrounding obstacles so that the generated waypoints are relatively equidistant to obstacles. On the other hand, straight lines connecting the waypoints in the Theta-star search cause sharp turns which are not feasible for non-holonomic robots. Thus, a smoothing method using ReedsShepp (RS) curves is utilized to produce kinematically feasible curves for non-holonomic robots. In the end, we demonstrate the proposed method in simulations via different scenarios. The method can be applied in diverse indoor applications, such as storage, indoor agriculture, and other applications.
Feature Word Extraction Method for Book Difficulty

With the rapid development of the E-Commerce market in recent years, product selection has become increasingly difficult. Therefore, various studies have been conducted to assist in the selection, but few have focused on the level of difficulty. However, if the level of difficulty related to content is not considered when selecting books, it is impossible to provide products that meet user needs. Therefore, this study used product reviews to identify the characteristic words for the difficulty level. As an extraction method, a distributed representation of words was obtained based on Word2Vec, and clustering was performed using DBSCAN with two-dimensional compression by t-SNE to provide stable clustering while considering the meanings of words. In addition, the experimental results show that it is possible to extract not only feature words directly related to the difficulty level but also words that indirectly affect the evaluation of the difficulty level.
Quantifying and Debiasing Gender Bias in Japanese Gender-specific Words with Word Embedding

Machine Learning is playing a significant role in modern life. However, the problem that Machine Learning has biases and stereotypes has also drawn the researcher's attention. Word2Vec, a popular framework in the NLP field to encode the word's meaning as a real-valued vector, has been used in many machine learning and natural language processing tasks. Still, it also has been proved that it contains severe biases toward women. In this paper, we used Word2Vec to analyze the relationship between gender-specific words and personality adjectives in Japanese to Figure out the latent gender bias in those gender-specific words. We first found that the Word2Vec model trained by Japanese Wikipedia data shows that some occupation gender-specific words strongly connect with negative personality adjectives. The experiment results reflect that people commonly use these gender-specific words to criticize women in these specific occupations. Then we eliminated the projection of word vectors of personality adjectives on the gender subspace and reduced the relationship between negative personality adjectives and gender-specific words by word vector calculation.
Ball Trapping in the Simulated Soccer using Decision Tree based Learning to Rank

In a group ball game such as soccer, the ball passing behavior between players is important for achieving cooperative team behavior. To acquire the ball passing behavior, conventional approaches mainly apply search and machine learning to the decision making of the players who perform the passing action. On the other hand, the position and posture of the pass receiver player when receiving the ball have not been studied sufficiently. This paper proposes a machine learning method using decision tree based learning to rank to select a more advantageous ball trapping behavior. We use the RoboCup Soccer Simulator as an experimental environment to collect training datasets and to evaluate the performance of the action selection model.
Mitigating Dataset Bias via Image Translation

Deep neural networks (DNNs) have recently emerged as the de facto standard for achieving exceptional results and demonstrating a major impact on a variety of computer vision tasks for real-world scenarios. Nonetheless, the trained networks frequently suffer from a well-known issue, overfitting, due to the unintended bias in a dataset that causes unreliable results. In order to overcome this challenge, several research have tried to relieve the bias by learning debiased representation with biased datasets; however, it still produces unsatisfactory results as it is difficult to learn the debiased representation in highly biased datasets. To address this problem, we propose a novel Image-to-Image translation framework, Biased Image Translation (BIT), that translates biased samples (bias-aligned) into bias-free samples (bias-conflicting). BIT consists of three steps: 1) extracting bias-conflicting samples, 2) training and adapting generative models, and 3) translating bias-aligned samples into bias-conflicting samples with the generative models. Finally, we can generate bias-conflicting samples in highly biased datasets without any prior knowledge about bias types. Through the bias benchmark datasets, composed of synthetic and real-world images, we demonstrate BIT successfully mitigate widespread bias issues by augmenting bias-conflicting samples based on a image translation mechanism.