Reviewerly/exHarmony huggingface dataset:
Video Analysis to Detect Dress Code Violations in Laboratories

The rapid advancement of image and video processing technology has led to a significant increase in its application. Particularly during the global COVID-19 pandemic, the automated analysis of camera footage has gained paramount importance in the automatic detection of events. This includes identifying violations or suspicious occurrences to ensure safety, such as the detection of mask or personal protective equipment (PPE) usage in controlled areas. Aligned with this context, the objective of this study is to develop a real-time video analysis system for object detection, specifically focusing on monitoring compliance with dress codes in laboratory settings. The system utilizes a combination of pretrained YOLOv3, Caffe, and custom-trained Mobilenet models to identify individuals who are not wearing face masks or lab coats. The custom Mobilenet models were trained using two datasets, achieving an accuracy rate of 0.81 for face mask detection and 0.92 for lab coat detection, surpassing the minimum objective of 0.80 for both categories. These promising results underline the potential of this approach in significantly enhancing safety within laboratory environments.
Expense Tracking with Tesseract Optical Character Recognition v5: A Mobile Application Development

Tracking daily expenditures is fundamental to managing personal finances. There are many ways to track personal expenditures. The common but inconvenient ways are manually recording expenses using paper and pen or note-taking applications on mobile phones. Thus, this preliminary study aims to develop a convenient Expense Tracking Mobile Application to track users” daily expenses and visualise their spending data using charts and statistics. Users can extract expenses from receipts using Tesseract Optical Character Recognition (OCR) version 5, which implements Long Short-term Memory (LSTM). The mobile application was evaluated using 20 receipts from grocery stores and markets. The result showed that the average Character Error Rate (CER) and Word Error Rate (WER) for items in receipts were 9.36%, and 19.39%, respectively. At the same time, the average CER and WER for unit prices in receipts were 2.26% and 4.33%, respectively. The mobile application was also evaluated using the System Usability Scale (SUS) and obtained a satisfactory score of 71.5%.
Ecologization of the Course of Organic Chemistry, Forming Ecological Knowledge and Education

The relevance of the research in this article is conditioned upon the problems of introducing environment-oriented education in the field of chemical discipline for the development of an important component of the information part of this science among students, based on its application in the framework of restoration and conservation of ecology. The purpose of the article is to develop a model of ecologization of the organic chemistry course for the development of knowledge in the discipline with the establishment of ecological culture. The leading method to study this problem is a combination of the Schwartz value questionnaire to determine the level of personal manifestations in the field of various behavioural reactions and decision-making, based on value motivational aspirations, which will affect social life and many of its components. The article presents the developed model of ecologization of the course of study within the framework of organic chemistry, which allows students to form an ecological culture and present the information part in the form of special knowledge based on the selective structure, which carries the factor of restoration and preservation of ecology.
A Simple Discrete-Time Recurrent Neural Network and its Application

This paper studies a simple recurrent-type neural network characterized by sparse binary connection parameters and signum activation function. Depending on the parameters, the network exhibits a variety of rotation-type periodic orbits of binary vectors. The network is well suited for precise analysis and FPGA based hardware implementation. Performing precise analysis in 8-dimensional cases, we have clarified the number and stability of the periodic orbits. Some of the orbits are applicable to control signal of octopod walking (spider) robots.
Enhancing MobileNetV2 Performance with Layer Replication and Splitting for 3D Face Recognition Task Using Distributed Training

In this paper, we propose the operators, which are layer replication and splitting, to modify the CNN network. The algorithm for exploring suitable modifications to the prototype MobileNetV2 architecture for the 3D face recognition task is presented. The algorithm consists of two steps. The first step involves splitting the input and searching for the optimal position to expand the network through layer replication. The second part explores the architecture modification by altering the split input layer position within the model. Experiments demonstrate that the discovered model provides a more cost-effective performance, with only 0.015% increased size compared to a vanilla MobileNet V2, while delivering 6.99% higher accuracy than the previous 3D MobileNetV2 and 9.15% more accuracy than the vanilla MobileNetV2, with a total of 3,801,136 parameters. Using distributed data training on multi-GPUs, the total training time can be reduced by 75% while maintaining good accuracy compared to traditional single-GPU training.
Two Autoscaling Approaches on Kubernetes Clusters Against Data Streaming Applications

This paper is aimed to study the performance comparison of two autoscaling approaches against data streaming and visualization applications under Kubernetes clusters. In particular, vertical and horizontal scaling methods are focused. The experimental setup has been done extensively based on the architecture stack which includes Apache Kafka, Apache Spark, Filebeat, Logstash, Elasticsearch, Kibana, Prometheus, and Grafana. The performance and resource utilization of the data flow display system in Kubernetes clusters are evaluated using Apache JMeter. Three metrics: CPU utilization, response time, throughput, and error rate are used. The experimental results show that on average, the CPU utilization of the horizontally scaled system is higher by 18.48% compared to the non-scaling system, while the vertically scaled system uses 49.45% using less CPU resources than the non-scaling system. The average response time of the horizontally scaled system is 65.60% lower than the non-scaling system, while the vertically scaled system is 1.19% lower than the non-scaling system. The error rate of the horizontally scaled system is 0.04%, but the error rate of the vertically scaled system is as high as 15.16%.
Hybrid Tow-Stream Information for Skeleton-Based Action Recognition Using Ensemble Learning

Skeleton data, a topological representation of the human body with joints and bones, are lightweight and compact, and thus are ideal candidates for action recognition. Ensemble learning has been shown to improve predictive accuracy or decompose a complex, difficult learning problem into more manageable sub-problems. However, studies have yet to attempt to utilize ensemble learning in GCN-based action recognition. This paper investigates the feasibility of ensemble learning on the GCN-based human action recognition task. Moreover, we start with data diversity aspects, i.e., first-order and second-order information, and utilize ensemble learning to improve the GCN model's recognition performance and generalization ability. First, we treat different GCN models as base models, such as ST-GCN, Js-AGCN, and Bs-AGCN, and extract the first-order and second-order information of the skeleton data using them. Then, two ensemble learning algorithms, i.e., Bagging and Blending algorithms, are utilized to ensemble the two-stream information. Experiments on the partial Kinetics-Skeleton dataset demonstrate that the performance of the ensemble model exceeds substantial improvements over the base models.
Emotional Intensity-Aware Learning for Facial Expression Manipulation

The advancement of deep learning has significantly improved the performance of generative models, enabling realistic facial image creation and natural translation of specific facial attributes. Among various tasks, facial expression manipulation (FEM) aims to naturally synthesize facial expressions that meet user requirements. While FEM research demonstrates good performance in image-to-image translation, there are still limitations in representing facial expressions across all frames within a sequence for video-to-video translation. Inspired by the sparsity of emotion-expressive frames in a sequence, we propose Mutual Information-based Emotional Intensity-aware Learning (MIEL), which enhances learning by reconstructing sequences using key frames based on point-wise mutual information (PMI). Our method selects key frames considering valence-arousal (VA) values for emotional intensity. The qualitative results show that MIEL can express emotions more clearly than existing techniques in all six emotion classes, indicating that the mutual information-based key frame selection scheme has enhanced the effectiveness of emotion representation learning and can be represented at the pixel level.
Pose Capturing and Evaluation in a VR Environment

This proposed method contributes to the existing body of knowledge and lays a strong foundation for future advancements in the field of VR-based pose analysis and interaction. As the technology continues to evolve, it is expected that further improvements and innovations will emerge, further enhancing the capabilities and applications of pose capturing and evaluation in VR environments. In this study, we introduce an innovative method for evaluating and comparing human postures by harnessing the power of optical motion capture and virtual reality (VR) technologies. Our cutting-edge pose matching algorithm enables users to refine their performance by providing real-time feedback on their postural alignment with a template pose. Capitalizing on the high accuracy and low latency of optical motion capture systems, our approach records detailed posture information, including time stamps, frame indices, and the orientation and position of multiple body joints in Cartesian coordinates. The algorithm computes a similarity score between the user's pose and the template by calculating a normalized loss function based on their 3D posture data. We seamlessly integrate the evaluation model into a VR environment tailored for posture imitation exercises. The template pose is pre-recorded, and the user's pose is dynamically synchronized with the physical world, visualized as an interactive 3D humanoid model within the virtual space. As users mimic the displayed postures and movements, the system generates instantaneous feedback on the similarity score, empowering them to refine their technique and enhance their performance, all within a safe and immersive virtual setting. As a result, we have developed a versatile VR application that successfully compares the similarity of postures and provides users with valuable feedback to improve their skills. The application demonstrates the efficacy of our pose matching algorithm and serves as a foundation for further development and expansion into various domains, including sports training, rehabilitation, and performance arts.
Development of Computer-Aided Diagnosis System Using Single FCN Capable for Indicating Detailed Inference Results in Colon NBI Endoscopy

In this paper, we propose a single fully convolutional network (FCN) capable of indicating the detailed inference results for Computer-Aided Diagnosis (CAD) in colon Narrow Band Imaging (NBI) endoscopy. The proposed CAD system is capable of real-time processing with a latency of 0.05 seconds and 20 frames per second and can detect more than 80% of lesions even for non-magnified images. Classification results at the pixel with the highest confidence level at resulted in a diagnosis with 73% agreement with histopathologic findings.
A Study and Exploration of Discrete Wavelet Transform for Speckle Noise Reduction in Ultrasound Images

This study aims to compare different discrete wavelet transform for speckle noise reduction in ultrasound images. A set of ultrasound images were filtered using Haar wavelet, different scaling of three basic wavelet bases, Duabechies and Symlets wavelet. We conducted an experiment by adjusting the value of the constant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> by proposed thresholding rule. We used objective metrics to evaluate our analysis namely Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structure Similarity Index (SSIM) and Coefficient of Correlation (COC). The experimental results demonstrate that the proposed thresholding rule leads to improved image performance compared to existing VisuShrink thresholding rule ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=1$</tex> ). In addition, the results show that the optimal value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> for all wavelet families used in this study is between 0.5 and 0.7. The sym8 provides the best performance compared to other wavelet types used in the experiment.
Mining of High Average-Utility Alarm Rules in Telecommunication Network Data

Mining huge number of alarm events data collected at the network operations center of a mobile network operator has become a challenging problem in the mobile communication area due to the fact that alarm events are heterogeneous and that they have different significance levels and may occur more than once in a certain time-window. In this study, the High Average-Utility Itemset Mining (HAUIM) approach is adopted to identify high average-utility itemsets in the alarm events data collected at the network operations center of Turkcell, which is a major mobile network operator in Turkiye. Moreover, a new interestingness measure has been proposed to obtain association rules between high average-utility itemsets. Experimental results have shown the efficiency of the proposed system with respect to compression and prediction performances.
An Improvement of Algorithm for Computing Final Exponentiation for Pairing on KSS36 Curve and its Implementation

Pairing is a bilinear map using two rational points on an elliptic curve and consists of two steps: Miller's algorithm and Final exponentiation. The Final exponentiation is an exponentiation step on an extension field, which can be decomposed into Easy and Hard parts. Since the Hard part requires more computational costs than the Easy part, the decomposition method and calculation procedure of it greatly affects the performance of the Final exponentiation. In previous studies, several methods have been proposed for decomposing the Hard part, including p-adic expansion method, lattice-based method and method using the logical algorithm. There are also known methods to search for the optimal calculation procedure, such as using addition chains or finding relations that allow reusing the calculation results. For the pairing on the KSS36 curve, p-adic expansion is proposed by Guzmán- Trampe et al. On the other hand, the authors propose a more efficient algorithm using a lattice-based method on the KSS36 curve at a 256-bit security level. The authors also implemented the algorithm, leading to a 1.68 % reduction in the execution time of the Final exponentiation from the previous one.
A Selective Multi-Unit Group Filter-Based Flexible Fast Light-CNN Training

Grouped convolution is a method extensively used in the design of Convolutional Neural Network (CNN) architecture. Its independent divided grouped filter facilitates the learning and provide possibility of learning various type of features. This reduces the number of parameters and computation costs, making it particularly useful for design lightweight CNN models, which is applicated for mobile devices. While several techniques such as group-based network pruning have been proposed to further improve its efficiency, these methods often require complex additional algorithms that could be challenging to implement on hardware. In this paper, we propose Multi-Unit Group Filter Block (MUGB), which is an efficient and simple grouped convolution method without the requirement of additional algorithms to improve the performance. MUGB can provides various combinations through selectively activate multiple grouped filters in response to the learning difficulty of the dataset or the target learning speed of the operation. This fast method of training that specialized in limited resources, and dataset, based on small-scale CNN models, could maintain high performance compare to other methods of similar compression ratio. At the compression rate of 81.2%, our proposed model achieves an accuracy of 1.5% higher than a similarly-sized CNN model, and 3.5% higher than a pruned model, while maintaining the performance of the uncompressed standard CNN model.
In-Cache Processing with Power-of-Two Quantization for Fast CNN Inference on CPUs

Convolutional Neural Networks (CNN) demand high computational capabilities, motivating researchers to leverage Processing-In-Memory (PIM) technology to achieve significant performance improvements. However, implementing complex arithmetic operations such as multiplication within memory is a significant challenge in the PIM architecture. To address this challenge, this paper proposes a PIM-enabled cache (PEC) architecture that utilizes shifters for performing multiplication operations at a low cost. We also introduce a filter-wise hardware-friendly Power-of-Two (POT) quantization scheme that quantizes weights into power-of-two values for specific filters to accelerate convolution operations with the PEC. Our experimental results demonstrate that the proposed PEC, together with the POT quantization, achieves 2.28x performance improvement on average with an accuracy degradation of 0.784%.
Video Semantic Segmentation for Intersection by Domain Adaptation

In recent years, semantic segmentation, which is one of the image recognition technologies for automatic driving, has attracted attention. Semantic segmentation can perform high accuracy object detection by discriminating classes with pixel level precision. However, training data for segmentation usually requires extensive manual labor because a corresponding label has to be assigned to each pixel. Currently, using a computer graphics (CG) dataset makes it easier to create supervised data. However, when inference images are real-world, intersection over union (IoU) is greatly reduced by the variation of the domain. In this work, an unsupervised domain adaptation (UDA) training method is proposed to achieve efficient intersection video segmentation models for each target domain. The inference is performed for intersection images with relatively complex situations. The simulation results show that the proposed algorithm for semantic segmentation of intersection videos achieve IoU scores comparable to supervised learning for classes that are prominently displayed on the screen.
Proposal and Quality Evaluation of Skin Imaging Using Smartphone and Ring Light with Polarizing Film

A method for skin image acquisition for cosmetic purposes using a smartphone camera is discussed. A simple construction using a commonly available ring light and a polarizing film is used to create an inexpensive method for capturing images comparable to those of expensive beauty and medical cameras. The use of a ring light stabilizes the light source, and a polarizing film mounted orthogonally to the ring light and lens eliminates skin reflection. Image quality was evaluated using the non-reference image quality evaluation model (Naturalness Image Quality Evaluator). We show that the proposed method provides resolution and image quality comparable to beauty and medical cameras.
Real-Time Inference Platform for Object Detection on Edge Device

Deep Neural Networks (DNNs) that perform object detection, such as autonomous driving, facial recognition, and medical healthcare, have received great attention recently. Due to the large amounts of data used in object detection DNNs, a cloud computing system for AI with centralized computing power and storage capacity has been used. However, with the increasing number of edge devices in the IoT trend and the growing amount of data, cloud-based AI encounters a challenge of network latency in processing real-time inference. In this paper, we propose a platform consisting of an edge device with a DNN inference accelerator and an optimized network to address the latency issues and achieve real-time inference of DNNs. The proposed platform adopts SqueezeNet, which is suitable for mobile devices due to its smaller network size than other DNNs. Post Training Quantization compresses the pre-trained SqueezeNet model size without accuracy loss. With the compressed network, an xczu3eg chip-based MPSoC board that includes an AI accelerator is used as the edge device. To further improve inference throughput, multi-threading is also used to reduce the latency between the Processing System(PS) and Programmable Logic(PL). Through the proposed platform, we achieve a 55 frame-per-second(fps) throughput, which is a sufficient real-time object detection inference performance.
A Proposal of Eliminating Fruitless Cycle for Efficient Pollard's Rho Method by Adding a Constant Rational Point

The security of elliptic curve cryptography is based on the ECDLP. The Pollard's rho method is known to be the most efficient attacking for ECDLP. In addition, the Pollard's rho method with skew Frobenius mapping, which is even more efficient version of the Pollard's rho method, however the number of fruitless cycle increases significantly. In this paper, the authors have succeeded in reducing them by adding an operation of addition and subtraction of constant rational points to Pollard's rho method with the skew Frobenius mapping.
Parameters Determination for Ill-Defined Edge Detection Using Particle Swarm Optimization

Segmentation process is one of the preprocess steps in object detection. To achieve segmentation, edge detection is a possible choice. However, if there is noise in image, edge might be ill-defined. Our algorithm for ill-defined edge detection is enhanced in this paper. In particular, we utilized the particle swarm optimization (PSO) in finding optimal parameters in the algorithm, rather than using manual settings as in the original work. There are two data sets, i.e., synthetic and carpal bone data sets, used in the experiment. We found that the intersection over union (IOU) on the blind test data set of the synthetic data set is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.9200\pm 0.0144$</tex> . The result on the blind test carpal bone data set is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.9228\pm 0.0592$</tex> . For the carpal bone data set, we compare the result with that from the original algorithm. The result shows that the enhanced method performs better than the original one. However, there is still a problem in misleading edge direction because of gradient and edge map generation.
Proposal of Flicker-Based QR Code with Invisible Information Display Lighting Device

This paper proposes a special QR code by using a LED-based display Stego-panelV. This QR code is invisible to the human eyes. On the otherhand, this can be recognized by a camera equipped with a CMOS image sensor. However, at this time, shutter speed of the camera must be adjusted appropriately. Therefore, by using this system, it is possible to improve the security by making it difficult to duplicate the QR code and secretly photograph it. To achieve this, we develop Stego-panelV, which has a special LED control mechanism and is operated by dedicated software, and conducted an experiment to evaluate the effectiveness of the OR code.