Reviewerly/exHarmony huggingface dataset:
Image Remapping Data Augmentation Approach for Improving Fisheye Face Recognition

Fisheye cameras present a challenge for face recognition due to their wide-angle perspective and image distortion. This paper introduces a novel approach to enhancing training data for fisheye-based face recognition without requiring image calibration. We employ five image-remapping transformations to diversify and expand the training dataset and evaluate the effectiveness of this approach using deep learning networks: HRNetV2 and ResNet50. The results demonstrate significant improvements in classification accuracy when utilizing authentic fisheye facial data. Specifically, HRNetV2 exhibits an increase of 30.2%, while ResNet50’s performance improves by 11.8% compared to their respective baseline performances. This study presents a fresh method for refining face recognition in fisheye camera scenarios, thereby extending its potential for real-world applications.
Vehicle Detector Based on Improved YOLOv5 Architecture for Traffic Management and Control Systems

Vehicle detection is an important module in traffic management and control systems. These systems require compactness, mobility, and high accuracy when deployed in a real-time context. Based on the YOLOv5 network architecture, this paper proposes several improvements to increase the performance and speed of the network when applied to vehicle detection. The research aims to redesign the backbone and neck modules with lightweight convolutional network architectures such as EfficientNet, PP-LCNet, and MobileNet. In addition, the Squeeze-and-Excitation (SE) attention architecture is also used inside the above-mentioned architectures to help the network focus on salient information during feature extraction. The network is trained and evaluated on a modified and normalized dataset of the UA-DETRAC dataset. As a result, the proposed network achieves 58.1% of mAP@0.5 and 40.1% of mAP@0.5:0.95 with just over ten million network parameters. This result outperforms other methods and is comparable to the lightweight architectures of the YOLOv5 family.
Virtual Sensors With 3D Digital Human Motion for Interactive Simulation

This article presents a design of “virtual sensors” to collect low-dimensional sensor data from 3D digital human motion and create real-world applications through the “interactive simulation.” It provides an opportunity to decrease dependency on real-world data requirements and gives more flexibility in the corresponding human activity-related applications.
Evaluation of Communication Overhead for Distributed Deep Learning for Local Data Privacy

In recent years, there has been growing interest in using big data for machine learning applications. However, as data are often distributed across multiple clients, privacy and physical limitations can make it difficult to aggregate and use these data on centralized servers. Federated learning and split neural networks enable machine learning models to be built without requiring the aggregation of distributed data. These methods have received increased attention in the industrial sector as a means of eliminating impediments to privacy preservation in data sharing across various institutions, such as smart factories. One critical aspect of implementing such methods in practice is analyzing the time variation of traffic and estimating the throughput required for operationalizing machine learning. Previous studies have formulated traffic characteristics without considering packet overhead and measured only the total traffic and the throughput per iteration; such an approach is inadequate from a traffic monitoring perspective. To address these issues, this study conducted an empirical investigation of traffic patterns under realistic conditions, with a particular focus on quantifying traffic and latency. The results of this study suggest that due to the dynamic nature of throughput, it is essential to measure traffic using time units that can accommodate the temporal variability of traffic.
Large-Scale Electric Vehicle Access to the Grid Requires Addressing the Threat of Disinformation

Electric vehicles (EVs) are widely adopted to reduce carbon emissions generated by the transportation sector. Coupled with green policies and government subsidies, the market share of EVs has been increasing in recent years. The increased penetration of EVs also requires the existing grid system to cope with the access of large charging loads, which may introduce new security risks to the electrical infrastructure. While numerous literatures have considered how to respond to these challenges, e.g., vehicle-to-grid (V2G) technology, tariff incentives, etc., few studies have considered the impact of changes in consumer behavior on the grid operation. In this paper, we design the following attack scenario: attackers manipulate consumer behavior by spreading disinformation in social networks to induce more EV owners to shift their charging time to the peak electricity consumption period, thus increasing the peak load. We conducted a survey in mainland China to investigate the propensity of consumers to receive such information and to analyze the impact of disinformation on social networks. In addition, we integrated the stochastic issues involved in the EV charging process to construct a charging load prediction model. By modifying the IEEE European LV test feeder to the Chinese standard and simulating it on this topology, we analyzed the destructive power of the attack on the grid and its potential impact.
Should I Sample it or Not? Improving Quality Assurance Efficiency Through Smart Active Sampling

The digital transformation provides industries with unparalleled opportunities for value creation. AI and Machine learning (AI/ML)-driven approaches for data analysis applied to the massive amounts of data steaming from industrial processes can lead to enhanced operation, costs reduction, and powerful decision-making strategies. In this paper we address the problem of Quality Assurance (QA) in industrial manufacturing. We propose Smart Active Sampling (SAS), a new QA sampling strategy for quality inspection outside the production line. Based on the principles of active learning, an AI/ML model trained for quality prediction decides which produced pieces or samples are sent to quality inspection, to further improve its own prediction accuracy. SAS reduces the production of scrap parts due to earlier detection of quality violations. By inspecting a much lower number of samples as compared to traditional random sampling approaches, SAS improves QA efficiency and cuts down quality inspection costs, resulting in an overall smoother operation. We elaborate on some of the challenges faced in smart sampling strategies for quality inspection, describe the main concepts behind SAS, and showcase its application in a real-world manufacturing QA use case, training an AI/ML model for product defect prediction. Compared to a standard random sampling strategy, widely applied today in industrial QA applications, SAS improves model prediction accuracy requiring a significantly lower number of inspected samples, up to five time less samples in the analyzed dataset.
Skill Level Classification Using Motion Data via Spatial Temporal Graph Convolutional Network

A skill level classification method using motion data based on Spatial Temporal Graph Convolutional Network (ST-GCN) is presented in this paper. Main contribution of this paper is realization of the accurate skill level classification by considering time-series characteristics. Motion data are closely associated with behaviors and tacit knowledge necessary for the classification of the skill levels. In addition, ST-GCN enables classification considering time-series characteristics to achieve high classification performance. Then the proposed method performs the skill level classification by ST-GCN using motion data. Consequently, the proposed method realizes the performance improvement of the skill level classification.
High Secure Mobile Wallet for Token Transfer using Escrow Account on PoA Voting Blockchain

Cryptocurrency wallet is very important to protect own cryptocurrency asset today. Even browser supports cryptocurrency wallet as Metamask now. But these wallets haven’t good security policy yet. The requirement of security wallet is 1) Protect mobile wallet from any hacking action. 2) Avoid mistake to transfer cryptocurrency to wrong receiver wallet address. But the issue of security wallet is 1) Black list is required update of latest list always to protect from hacker. 2) There is no way to avoid mistake to enter the receiver’s wallet address incorrectly. Sender should double check the address or do the test transfer before sending the complete amount. Our proposed voting blockchain has escrow account node to judge sender wallet node to receiver wallet node. This paper shows the details of this secure wallet and shows how escrow account node on voting blockchain works.
IDrugLabeling: A Computer Vision-based Automatic Medicine Training Data Labeling System

This paper proposes iDrugLabeling, a computer vision-based automatic medicine training data labeling system. iDrugLabeling comprises a camera module, a light source module, an electric rotating platform, and an edge-computing module. As such, iDrugLabeling can capture images of medicines from various angles under fixed shooting distances and brightness. Moreover, iDrugLabeling can detect and label the positions of medicines within the captured images using a feature extraction and matching scheme to generate training datasets for medicine recognition. Experimental results demonstrate that iDrugLabeling achieves an average Intersection over Union of 0.85 for labeling boxed medicines, surpassing manual labeling. Additionally, it achieves a labeling accuracy rate of 0.998 for labeling aluminum-foil sealed tablets, further confirming its effectiveness in automating the collection of medicine training data.
Solar PV Power Forecasting and Ageing Evaluation Using Machine Learning Techniques

Solar photovoltaic (PV) power forecasting is a crucial aspect of efficient energy management in the renewable energy sector. This study examines the use of artificial neural networks (ANNs) to forecast solar PV power output. It considers various factors influencing power output and investigates different ANNs for prediction. Real-world PV power data is collected and preprocessed for training and testing ANNs such as recurrent neural networks, autoencoders, and convolutional neural networks. The results show that ANNs, particularly Long Short-term memory (LSTM), accurately forecast PV power output in the short term. The study also analyzes the impact of panel ageing on PV power using machine learning models, revealing effective prediction of performance degradation. Clustering the dataset into sunny and cloudy subsets, and using separate models for each subset improves prediction accuracy. The study presents a comprehensive analysis of ANNs for PV power forecasting and the influence of panel ageing, highlighting the potential of machine learning for precise and reliable predictions.
Unsupervised Low Light Enhancement Method with Inherent Diffuse Map

Paired training is a widely-used approach in low light image enhancement(LLIE). However, normal light image with pixel wise calibration cannot be obtained at night in real scene. Then training with low light images only can be a possible solution. The generalization of low-light-image-training-only methods is also an important task, since the different training data produces different LLIE performance. To solve this problem, a diffuse map which is regarded as inherent consistent texture feature, is provided in our low stream network as guidance. Then an image-to-curve transformation is adopted achieved with our up stream to produce the final result, which is consistent with low light to extreme low light condition. A set of experiments have validated the image restoration and generalization abilities of the our method.
A FPGA-based System Implementation of Single Image Haze Removal using Improved Dark Channel Prior

Removing haze from images remains an ill-posed challenge in image processing. The dark channel prior-based dehazing algorithms demonstrates favorable outcomes for a wide range of scenes, despite the inherent impact of haze on image quality. However, the DCP algorithm may struggle to accurately remove haze and produce artifacts in scenes with extremely bright regions, leading to potential shortcomings in haze removal effectiveness. In this work, we combine a bright channel prior with an adaptive blending to enhance the transmission map refinement. The proposed method is evaluated using a widely accepted single image dehazing benchmark, then deployed on Field-Programmable Gate Array (FPGA) platform to produce haze-free output with better details in real-time.
Online Deep Learning Education Method Based on Prototype Construction and Its Practical Evaluation

Deep learning (DL) education is being promoted in the curriculums of universities and large companies. However, in companies that have outsourced their IT departments, engineer training environments for improving the added value of products and services through DL remain insufficient. Furthermore, building a DL system requires DL-specific considerations, such as data preparation and model tuning, to ensure quality. Based on the circumstances described above, we propose a practical DL application education method based on the construction of a prototype system for DL application, and discuss its practice and evaluation.
Training Large Kernel Convolutions with Resized Filters and Smaller Images

Convolution is an essential component in neural networks for vision tasks. Convolutions on large receptive fields are more suitable than small kernel convolutions when aggregating global features in convolutional neural networks. However, large kernel convolutions require much computation and memory usage, slowing training neural networks. Then, we propose to train convolution weights with small images, resizing the convolution filters. While this idea shortens the time for training filters, simply applying this causes profound degradation. In this paper, we introduce four techniques that suppress degradation; weight scaling, removing Batch Normalization, defining a minimum resolution, and training with various-size images. In our experiment, we apply our proposals to train an image classification model based on RepLKNet-B on the image classification task of the CIFAR-100 dataset. Training with our proposals is approximately eight times faster than conventional training on the target spatial scale, keeping its accuracy.
Harnessing Recurrent-Based Deep Learning Models for Time Series Photovoltaic Power Forecasting

Photovoltaic (PV) power is progressively being subsumed into power grids. Consequently, reliable PV power forecasting (PVPF) has become essential to avoid ramp events that can adversely affect the operations of integrated power systems. This article presents a deep-learning-based algorithm for PVPF. The gated recurrent units (GRU) network was implemented to predict the non-linear spatiotemporal correlations of the weather data, leading to higher reliability of the PV stations. Experimental results obtained from actual testing demonstrate the validity of the GRU networks for accurate PVPF, contributing to the efficient operation and management of smart grids and renewable energy systems. The conducted case study shows that the proposed model outperforms bidirectional long short term memory (BiLSTM) and long short term memory (LSTM) models in terms of computation power, root-mean-square error, and mean absolute error metrics.
Study on Acoustic Anomaly Detection for Watching over Daily Life of Elderly People

We developed an acoustic anomaly detection system to watch over elderly people in their daily lives. A comparison of our proposed method with that of an existing system employing neural networks demonstrated the superiority of our proposed method.
Relationship Between Impression Change Degree of Music by Adding Video and Cumulative Contribution Ratio of Principal Component Analysis

For content-creating work that combines music and video, this study considers improving the efficiency of the video selection process by estimating in advance how much the impression of the music will change with the addition of video. We paid attention to the amount of information had by both the music and videos and assumed that the impression change degree of music when video is combined to music, is determined by the interrelationship between the diversity or complexity of information of music and videos. In this paper, we expressed the amount of information using the cumulative contribution ratio (CCR) of principal component analysis (PCA) and analyzed the relationship between the CCR of each music and video and the impression change degree when the music and video are combined. As results, (1) The music that CCR of the lower-order component is higher -- the amount of information is less --, the impression change more when video was combined. (2) The video image that CCR is higher, that affect to the music impression more strongly. These results suggest that the CCR of PCA would be a useful indicator for estimating the impression change degree of music when video is added.
Concept Drift Detection and Adaptation in IoT Data Stream Analytics

Dynamic nature of the IoT data can often cause machine learning model degradation, which can lead to analysis and actions failures. In order to address this challenge, it is of utmost importance to detect drifts in the data that can cause unwanted behavior of the system, and to update models accordingly. This paper gives an overview of different methods for addressing concept drift detection and adaptation, demonstration of some of the methods using open source libraries, and a design for a component for smart adaptive system for streaming data analysis that uses presented techniques to ensure high model performance.
Craft Visualization by Analyzing Emotional Information and Spatio-Temporal Features

This paper presents a visualization system aimed at enhancing users’ understanding of local traditional crafts and promoting their revitalization at a regional level. The system utilizes an analysis of emotional information and spatio-temporal features to visualize traditional crafts that have been officially designated by the Ministry of Economy, Trade and Industry (METI). By incorporating these analytical aspects, users can gain insights into the distinctive characteristics of local traditional crafts and the emotional attributes that reflect their historical significance. Furthermore, we developed a visualization system specifically designed for traditional crafts and conducted user satisfaction experiments to assess its effectiveness. The results of these experiments confirm that users can intuitively and comprehensively comprehend the intricacies of local traditional crafts through the utilization of the visualization system.
Abnormal Motion Detection Based on Derivative Dynamic Time Warping Using Motion Data of Bowling-Pitching Posture

Bowling has many elderly users who need to acquire the correct throwing posture to prevent injury. This paper presents a method to detect abnormal motion in the bowling throwing posture using anomaly detection. The distance is calculated at each data point by inputting the coordinate data of each body part obtained from motion capture into Derivative Dynamic Time Warping. Then, anomaly detection using the Hotelling theory is used to detect the movement of the anomalous part when the ball is thrown. The proposed method shows better results than other comparison methods of abnormality detection.
A Pin Classification Extraction Algorithm for Non-Standard Chips Based on Curve Detection

With the continuous development of the SMT (surface mounting technology), equipment circuit boards are gradually moving towards the direction of miniaturization and precision. Surface mounting technology is playing a more critical role in manufacturing. The main task of the core mounting machine is to realize the detection and identification of different chips, to ensure that the specified chips can be mounted stably with high speed and precision. With the development of patch chips, there are more detection methods for component pins. If a specific detection method is proposed for each non-standard component, the component detection will become more complicated. To solve such problems, this paper proposes an algorithm for extracting pins of non-standard chips based on curve detection, which aims to help users to teach different types of pins of non-standard chips by curve detection and use the teaching results as template matching to detect all non-standard patch chips. By mounting experiments on non-standard component,the test speed and production stability of the proposed algorithm have reached the leading level.
Proposal for Writing Authentication Method Using Sequential Characters in e-Testing

This study focuses on countermeasures against spoofing. Previous studies have confirmed that personal authentication, using handwriting information, is possible in e-Testing. However, they could only be applied to e-Testing in which the answer is given in one digit (0-9) because the authentication was performed using one digit. This study created an e-Testing system that answers 2-6 digits and proposed a writing authentication method using sequential characters in e-Testing with help of a pen-tablet PC. The experiment yielded that when authenticating with two characters (bigrams) instead of one character (unigrams), the accuracy was higher. Furthermore, it was confirmed that personal authentication is possible even in e-Testing that answers two digits or more.
VHDL Realization of a Hardware Accelerator for k-NN Classification

The machine learning has a very broad specter of application in modern world. One of the important roles of the machine learning is in process of classification. Since the classification is very computational heavy process, the hardware accelerator can alleviate the work of processor and improve the implementation of machine learning process. In this paper we present our hardware accelerator for the k-Nearest Neighbours (k-NN) classification. The results of the implementation on the FPGA confirm the functionality of this accelerator, and the results of the implementations for various values of the parameters show that the accelerator size isn’t affected significantly by them, so the parameters can be chosen according to the application demands.
Selective Domain Transformation Using Deep Learning for Improving Object Detection Accuracy

In this study, we aim to perform a high-precision domain transformation of data captured in any environment, including unfavorable ones, into data captured in a typical environment. This will improve the accuracy of various tasks, such as object detection. Our proposed method focuses on latent space distribution control when performing domain transformation. This method first forces the encoder, using instance normalization, to acquire a latent space strongly related only to the objects in the image. Furthermore, by minimizing the distribution discrepancy in the latent space between the input image and the image after domain transformation, domain information is excluded from the latent space, and subsequent domain transformation is facilitated. Furthermore, adaptive instance normalization achieves domain transformation that does not depend on the input domain. In the experiments in this paper, domain transformation was performed on road images and object detection was performed using a general model. Then, the proposed method is evaluated using this detection accuracy.