Reviewerly/exHarmony huggingface dataset:
Wireless Network Selection System Using Federated Learning Considering Applications in Use

In recent times, the spread of mobile terminals and the escalating data communication volume have engendered connectivity challenges in densely populated locales such as urban stations. Previous studies have approached this issue by conceptualizing it as a misalignment between base stations and terminals, thereby proposing a matching approach base on graph theory. However, even after mitigating the mismatches, this solution remains computationally intensive and may not be conducive to practical implementation. This research presents a novel approach leveraging federated learning and introduce a meticulously designed system that ensures both precision and expeditious processing. As an initial phase, we conducted simulations employing machine learning techniques, yielding credible and substantiated results.
Data Augmentation with Automatically Generated Images for Character Classifier Model Training

This paper presents a novel data augmentation technique crucial for training AI-OCR systems for handwritten character classification. Using a Y-autoencoder (Y-AE) enhanced with Adaptive Instance Normalization, diverse handwriting styles are generated to improve the breadth of handwriting representations. A filtering mechanism is introduced to include only valid character images for training. The method was tested on a subset of the ETL Character Database, featuring 92 unique Japanese Hiragana and Katakana characters. The baseline classifier achieved an accuracy of 0.9061. However, when using the augmented dataset, which included Y-AE model-generated and filtered images, the accuracy improved to a maximum 0.9555 with data augmentation technique. These results showcase the potential of this data augmentation technique in consumer electronics, particularly in AI-OCR software. Despite needing some noise removal, the approach significantly boosts classifier accuracy, suggesting an efficient way forward for document processing in various sectors.
A Model Development of Smoker Detector Captured in CCTV Footage using MobileNetv2-SSD Algorithm

The development of a model for identifying smokers in closed-circuit video (CCTV) footage is presented in this research work employing the MobileNetv2 - SSD (Single Shot MultiBox Detector) algorithm. The vitality of the research hinges in its ability to automatically monitor and enforce no-smoking policies in the Philippines, improving public health and safety. With the usage of this model, smoking infractions can be reduced while also alleviating the workload of manual monitoring while providing prompt solutions. According to the research’s finding, the proposed model exhibits excellent results in terms of accuracy, precision, sensitivity/recall, specificity, and F1-score evaluation. The following test results for the MobileNetV2 SSD algorithm were obtained for the detection of smokers: Accuracy: 0.9722, Precision: 0.9728, Sensitivity/Recall: 0.9722, Specificity: 0.9722, and F1-score: 0.9722. These findings demonstrate the developed model’s efficacy and durability in accurately recognizing smoking incidents in CCTV footage. This study’s findings pave the way for further research, such as adding multimodal data and building real-world applications of the smoker detection system.
A Novel Framework for Sentiment Based Product Recommendations Using User Reviews - A Case Study on the Steam Platform

This study aims to propose a novel framework for sentiment-based product recommendations using user reviews, with a case study on the Steam platform. In today’s digital era, user reviews have become an indispensable factor in consumers’ purchase decisions. However, traditional recommendation systems based on rating scores have certain limitations, such as the inability to capture users’ emotions and the strengths and weaknesses comprehensively. Therefore, we propose a sentiment-based product recommendation framework to better understand users’ emotional responses towards products and pro-vide personalized recommendations.
Writer Alternation Detection by Sequential Hotelling’s Theory for e-Testing

To prevent proxy test-taking during unsynchronized e-Testing, we examined a method for examinee alternation detection based on online handwriting information, incremental statistics, and Hotelling’s theory. In this study, Hotelling’s T2 and Hotelling’s transform (PCA) were compared, and the findings indicated that T2 had a lower calculation complexity and higher accuracy than PCA. In addition, the results showed that PCA reduced the rejection rate of earnest students.
Facial Attribute Recognition Using Lightweight Multi-Label CNN-Transformer Architecture for Intelligent Advertising

In modern cities, intelligent advertising platforms have been widely engaged in public areas. A facial attribute recognition technique is essential to assist these platforms in delivering suitable adverts for each audience. These platforms also require a recognition technology that can operate at least suitably on a CPU device to reduce implementation costs. This work proposed a lightweight multi-label CNN-Transformer architecture with an efficient inception block (EIB) and squeeze channel transformer encoder (SCTE) to perform facial attribute recognition efficiently. EIB is used to extract face features in multi-scale and levels supported by SCTE in improving its feature map's quality. The proposed architecture produces fewer parameters with low operations and gains competitive accuracy on the CelebA and LWFA datasets consisting of images with multi-label. Moreover, the proposed architecture integrated with face detection can perform sufficiently on a CPU configuration in real-time with 21 frames per second (FPS) using 224 × 224 input size of face area image.
Multimodal Synthetic Dataset Balancing: a Framework for Realistic and Balanced Training Data Generation in Industrial Settings

Deep networks have been successfully applied to industrial applications for clean unimodal data (e.g., sensors, images, or audio). Leveraging multimodal data is a common approach to enhance performance, guided by the principle that a larger quantity of data leads to improvement. However, performance may decline considerably if corruption in the data is present (e.g., noise, blur, failure). Although researchers have explored various data augmentation methods to improve the generalization capacity, these methods are not adapted for industrial settings. The primary distinction is that current augmentation methods are designed to enhance model generalization capabilities and not realistically simulate real-world industry scenarios. In this paper, we present industry-related augmentation methods for temporal and spatial data for multimodal fusion with deep neural networks. Our methods are specifically designed to encourage modality collaboration and reinforce generalization capability. The impact of the proposed data extension strategy to train multimodal fusion models is assessed on a synthetic dataset from an industrial UR5 robot with varying degrees of imbalance. In our study, we analyze different combinations of methods and evaluate their performance. Through these experiments, we are able to identify the challenges in multimodal fusion with deep learning models in an industrial setting.
Enhancing Multi-Agent Reinforcement Learning: Set Function Approximation and Dynamic Policy Adaptation

While Deep Learning based methods can solve complex problems by employing Neural Networks to act as powerful function approximators, they often suffer from inflexibility in terms of deployment beyond the training scenario and also include irrelevant data priors in the form of an ordered array of input values. This problem is quite evident in the field of Multi-Agent Reinforcement Learning (MARL), where most research covers methods that are trained on a fixed number of agents, restricted by the fixed size of the input vector. In this paper, we argue that this is not a reasonable assumption, both in terms of the inflexible amount of environmental information and the restrictive nature of the structure of the information. We explore DeepSets and Set Transformers as two powerful set function approximators to address the problem of cardinality invariance and permutation invariance in the observation space of a reinforcement learning agent. We explain Set-Input Reinforcement Learning (SIRL) in detail and evaluate the performance of the DeepSets and Set Transformer methods through simulated experiments on a challenging multi-agent environment, that otherwise yields sub-optimal policies through traditional function approximation approaches. We demonstrate that both DeepSets and Set Transformer based encoders scale well to increasing the number of agents from training to evaluation.
Synthetic-MixUp: A Simple Framework for Imbalanced Text classification

This study focuses on analyzing effective strategies to handle imbalanced data in text classification. The study employs probabilistic Naive Bayes as a common machine learning approach and investigates two oversampling techniques: Random Oversampling (ROS) [4] and Synthetic Minority Oversampling Technique (SMOTE) [2]. The methodology comprises several stages, starting with preprocessing the data into vector representation using TF-IDF from document samples. To address the imbalanced data, we select some vectors from a single document and mix them with other documents to generate a new vector representation. Finally, the study employs the Naive Bayes (NB) [3] classification method. The experimental results confirm the efficacy of the proposed methods, showcasing notable enhancements in the performance of minority classes. By successfully addressing the challenge of class imbalance, our approach enables the development of accurate text classification models, Which can be observed from both NB+ROS and NB+SMOTE generally outperform the NB+Imbalanced Data that F1-Score, Average Precision and Balanced Accuracy around 83.65; 83.65; 85.8. From our approach, NB+SMOTE tends to yield better results compared to NB+ROS in most cases.
Structure and Attention Enhancement of YOLO based Mask Wearing Recognition

The global COVID-19 pandemic dramatically changes our mod-ern daily life, e.g., wearing a facial mask. Thus, mask wearing recognition brings a new challenge of deep learning. This paper proposes structure and attention enhancement of YOLO which is one famous real-time object detection algorithm. Based on our initial experimental results, the proposed enhancement can in-crease 9.1% accuracy and reduce 15.8% computational effort.
Text-to-image Diffusion Model Suppressing Catastrophic Forgetting via Elastic Weight Consolidation

This paper presents a personalized text-to-image diffusion model for multiple contents introducing Elastic Weight Consolidation (EWC) suppressing catastrophic forgetting. The proposed method can support personalization to multiple contents based on continuous learning, while conventional methods mainly focus on fine-tuning of a single object. Since the standard continuous learning has a problem of catastrophic forgetting due to the training without considering the importance of the previous tasks, we address this issue by adding a regularization term of EWC in the second and later fine-tuning. By introducing the EWC, the proposed method enables the suppression of the catastrophic forgetting in the text-to-image generation tasks. Experimental results show that the proposed method is effective for the catastrophic forgetting based on Frechet Inception Distance (FID) as a quantitative measurement.
Electric Guitar Strumming Sharpness Evaluation Based on Short-Term Collision Detection

A wrist-worn motion-sensing device with a high sampling frequency of 1024Hz has been investigated to evaluate the strumming sharpness of electric guitar. A newly developed algorithm to detect the collisions onto the strings of the guitar with the player’s hand and the plectrum. The duration of the successive peaks of the angular jerk signal around the player’s forearm has been determined to analyze the collisions. It was found that this microscopic collision-based analysis is effective to evaluate the sharpness of guitar strumming especially in a slow rhythm region where the former macroscopic time-integral algorithm showed inferior performance. A 14-week-long population test with 12 amateur guitar players showed that the microscopic analysis may illustrate how the sharp strumming skill development had been achieved.
Explainable AI for Industrial Alarm Flood Classification Using Counterfactuals

In this paper, we propose a novel method for enhancing the explainability of alarm flood classification results using and adapting concepts from the field of explainable artificial intelligence. Alarm flood classification methods are helpful in managing complex industrial processes; however, their predictions can be challenging to understand and justify, especially for operators without expertise in machine learning. Our proposed model-agnostic method generates counterfactual alarm floods to provide explanations for classification results obtained from any alarm flood classification model. By examining the differences between the original alarm flood and counterfactuals, we provide actionable insights for plant operators for decision-making and understanding the underlying dynamics of alarm floods. We demonstrate the effectiveness of our approach by experiments on three state-of-the-art alarm floods classification methods and an openly accessible dataset based on the “Tennes-see-Eastman” process, showcasing the added value of our method in improving the explainability and trustworthiness of alarm flood classification results.
Noise Specific Blind Image Quality Assessment Using Three Statistical Features

This paper presents a blind image quality assessment (BIQA) method for noise distorted images. In the proposed method, three different domains are simultaneously utilized and three statistical features are extracted from the input noisy image. The noise distortion types to be covered are more widened than those done by the conventional BIQA methods. Experiments demonstrate that the proposed method achieves more accurate BIQA from noisy images than the state-of-the-art methods do.
Private Reversible Aging-Aware Fuel Cell Prognostic: A Federated Multitask Learning Approach

Fuel cell prognostic has been considered an efficient way for the health management of fuel cell system and contribute to its lifespan. Nevertheless, accurate fuel cell prognostic is a challenge task because the lifespan of fuel cell is influenced by the operating conditions and there could be reversible aging due to the electrochemical reaction nature of fuel cell. In addition, conventional data-driven prognostic method trains a prediction model for each working condition without leveraging the common aging features for various conditions and thus may yield a less generalized model. What's more, training a promising data-driven prognostic model require large amount of data, which is hard to be collected by one institution due to the high cost and long-term degradation nature of fuel cell, whereas collecting aging data from different entities face the challenges of privacy concern. To address the above-mentioned issue, this paper proposes a private reversible aging-aware method. To address the reversible aging issue, a hint function based method is proposed to add indication terms in the input feature to help the model better deal with the reversible aging. After that, a multitask learning approach is considered to enable the institution to learn shared representations of the aging data, where the multitask learning is reformulated by federated learning to protect the data privacy of each institution. Experiment on the PHM2014 data challenge prove the effectiveness and robustness of the proposed method.
Virtual Touchpad for AR Glasses Based on Gesture Recognition

This paper proposes a control method called virtual touchpad for AR glasses which mirrors a screen of a PC. Using virtual touchpad, a user can control mouse cursor by gestures similar to the ones of a physical touchpad. Different from a physical touchpad, it is hard for users to recognize hand’s position and operation status. To solve these problems, we introduce the flexible position mechanism and visual feedbacks. From the result of volunteer’s test, we confirm that virtual touchpad is a good method to control AR glasses.
Inducing Unpredictable Behavior in a Walking Fly by Destroying Neurons in Its Neural Network

The rise of AI technology, especially neural networks, has earned significant attention as a powerful tool capable of solving a wide range of problems. However, I am intrigued by the idea of utilizing neural networks in a less practical direction. Specifically, I aim to simulate the aimless walk of a fly on a window glass using a neural network. Instead of relying on random values, I have implemented a technique called "neurodrug" where I deliberately destroy neurons in a neural network to generate unpredictable behaviors. Through this experiment, I delve into philosophical questions related to purpose, decision-making, randomness, motivation, intelligence, and instinct.
Transparent Transition of a User-Level Service to Kernel-Level Functionality

While there are a number of user-level services that extend the functionality of operating systems, they suffer from difficulty of development and performance degradation. This paper proposes a novel methodology that enables the transparent transition from the development of a user-level service to kernel-level functionality. By employing the proposed methodology, 1) a new service is efficiently developed at the user level, and then 2) the developed service is implemented as the kernel-level functionality for better performance. We realized the methodology on the Unikernel Linux (UKL) as UKL maintains the existing system call interface for user-level programs.
SWIM Service Trust Framework to Ensure the Safety of Flight Operations

To protect the safety of flight operations from cyber threats and ensure business continuity, a trust framework for international aviation that can ensure trusted information exchanged between trusted identities through trusted communication paths has been discussed by the International Civil Aviation Organization (ICAO). However, due to the information security failures affecting the aviation safety, not only digital identity and network resiliency but also access and safety risk management should be considered. In this paper, the security capabilities of System Wide Information Management (SWIM) supporting secure, high performing and reliable information exchange among related aviation stakeholders are clarified and validation assurance levels for safety-critical information are defined. Moreover, a new SWIM service trust framework model considering the cooperation between identity, access and risk management to ensure the safety of flight operations is proposed and evaluated.
Evaluation of Cognitive Assistance for Visual Impairment to Recognize Dynamic Walking Environments Using Vibrotactile Stimulation

This paper proposes an approach to assist the visually impaired in walking independently by presenting people flow and sidewalk edge information. To achieve this, a robust estimation of the direction of people flow, and a method for detecting the edges of sidewalks are presented. Image learning for sidewalk estimation is performed using binary semantic segmentation that classifies sidewalks and other areas to see sidewalks regardless of pedestrians and shadows on sidewalks. The effectiveness of the methods was evaluated. Then, preliminary user experiments will investigate the effect of the walking assistance information presented by this approach on the decision of walking behavior.
Augmented and Virtual Reality Simulation in Industry

Simulators have been used for a long time in industry. We identify three categories of use: training, design, and testing. We underline that for these three categories of uses, the two major areas of improvement are a better use of haptic devices but also of artificial intelligence.
Estimation Of Japanese Word Intelligibility Using CTC-based Phoneme Recognition

The purpose of this study is to estimate subjective Japanese word intelligibility using Connectionist Temporal Classification (CTC) based phoneme recognition. The Japanese Diagnostic Rhyme Test (JDRT) is one of the subjective evaluation methods to measure Japanese word intelligibility. We propose an objective method to estimate intelligibility from the output of automatic speech recognition (ASR) in an attempt to predict its subjective value. In previous studies, we attempted to use hybrid ASR and APIs for ASR in order to use the well-trained acoustic models used in these systems. However, since these systems were targeting large vocabulary recognition, the mismatch with the limited vocabulary in the JDRT task resulted in non-trivial errors, such as blank token output. CTC, which is trained by targeting the correct answer of the output text series rather than frame-by-frame, like HMM, is considered to be a more suitable method for estimating word intelligibility. In this paper, we constructed a phoneme recognition model using CTC and simulated the JDRT.
Constructing Data-Driven Personas for Effective Utilization in Social Network Service Activity Data

In today’s rapidly changing world, service operators must quickly grasp user requirements and modify services. Personas, which are virtual user images, are used to plan, modify, and develop software content. Previous studies have proposed data-based persona construction methods, but their applications to essential services are limited. Herein we propose a method to systematically utilize personas for planning content in social network services. Our method creates personas based on service log data and applies them to essential services by dividing users into groups using K-means clustering and building average and outlier user personas in the service. Then our method compares personas of average and outlier users. To evaluate the proposed method, we measured the click-through rate of users to the content that the service producer planned based on personas.
A Method for Removing Shadows and White-Outs on QR Code Images by Deep Learning

A method to remove shadows and white-outs on a QR Code using a deep learning model, pix2pix, is proposed. In this method, QR code images with artificial shadows and white-outs were created and employed for training. The experimental results confirm that the effects of shadows and white-outs on QR Codes can be almost eliminated.
Abnormal Event Detection of Tourist Attraction Traffic Fortress Based on YOLOv5-C3D

In recent years, with the rapid development of tourism, the abnormal events in the traffic fortress of tourist attractions not only endanger personal safety, but also cause a lot of negative effects on the society. In the face of the low accuracy of abnormal event detection in complex scenes and the low efficiency of manual observation, combining the advantages and characteristics of YOLOv5 and 3D convolutional neural network(C3D), an automatic abnormal event detection algorithm based on YOLOv5+C3D was proposed. Experimental data show that compared with other methods, the abnormal event detection method based on YOLOv5+C3D has a higher accuracy for various abnormal events detection, indicating that the trained model has a strong generalization ability for abnormal event detection.