Reviewerly/exHarmony huggingface dataset:
Rethinking on Ciphertext Equality Check of Decapsulation of NIST PQC Standardization 3rd Round Finalist Candidate Saber

In this paper, we rethink the ciphertext equality check step of the decapsulation procedure of Saber, which is one out of four KEM finalists in the third round NIST PQC standardization process. Through this investigation, we pursue cryptographic answers to some important questions. Those are why the re-encryption process of Fujisaki-Okamoto transformation, which is commonly used method to satisfy IND-CCA security in most existing lattice-based KEM algorithms including Saber, is especially necessary in the case of Saber, whether the re-encryption process can be further simplified, and whether the ciphertext equality check must be performed over the entire (re-encrypted/originally received) ciphertexts?
3D Human Body Pose Estimation in Virtual Reality: A survey

In Virtual Reality (VR), human pose estimation refers to the approximation of the location of body parts of a real human in a physical world. During the past decades, virtual reality has a widespread application in many areas such as the video game industry, user interfaces, and visual simulation. Hence the human pose estimation has recently attracted increasing attention in the VR community. However, it is challenging due to the variety of human movements. This paper discusses different methods for human motion estimation that have been investigated for years. We cover a variety of approaches, including motion capture techniques, inverse kinematics (IK), and the learning-based techniques that are the primary solutions to estimate the body joints in real-time in this scope.
A Preliminary Study for the Ethereum Blockchain-Based Smart Home Systems

The application of blockchain technology means that smart home devices will be greatly improved in terms of versatility and security, allowing people to use them more safely and conveniently in their daily home life. For smart home practitioners, blockchain technology means the arrival of the next “window”, indicating that the industry will develop from the budding stage to the popular stage. This paper proposes a smart home system based on the Ethereum blockchain as a platform. Through the use of smart contract technology, it not only builds a closed-loop management platform from energy suppliers to end users but also realizes cross-ecological communication. At the same time, this paper also examined the latency problem of “solidity” in the multi-contract call environment. By comparing the local pace test and the blockchain-side pace test, we found that the function invoked pace inconsistent between local-side and chain-side existed but the delay can be controlled within one second.
Automatic Modulation Classification using Relation Network with Denoising Autoencoder

With the advent of deep learning (DL), various automatic modulation classification (AMC) methods using deep learning architectures achieved significant performance improvements compared to conventional algorithms.Aiming to achieve high classification accuracy, DL-based AMC algorithms require numerous annotated training samples for each modulation class to extract salient features, but it is hardly applicable in real-world AMC applications.To tackle the annotated data scarce issue, this paper proposes a novel few-shot learning (FSL) framework, which introduces a relation network with a denoising autoencoder to extract feature representations effectively from a limited dataset.The experimental result demonstrate that the proposed method can achieve higher classification accuracy compared to the conventional FSL algorithm for signal modulation recognition, especially in low signal to noise ratio conditions.
SAR-to-Optical Image Translation Using SSIM Loss Based Unpaired GAN

The synthetic aperture radar (SAR) image can be acquired regardless of weather condition and time through radar reflection. However, visual effects and observations of the SAR image is difficult due to single channel information and speckle noise. In this paper, we present the disadvantage that cycle-consistency loss focuses only on texture based on the CycleGAN, and propose to apply the dual contrastive learning GAN (DCLGAN) that maximizes mutual information between SAR and optical images by learning the correlation of image patches. Also, we propose a method to apply the structural similarity index measure (SSIM) loss instead of cycle-consistency loss to address the lack of expressiveness by emphasizing structural features. The proposed DCLGAN eliminates cycle-consistency loss of CycleGAN and maximizes mutual information through contrastive learning to translates unpaired SAR-optical images. In addition, the proposed SSIM loss helps translate the SAR-optimal image by recognizing perceived changes in structural information. We confirm the performance through comparative experiments with existing models to verify unpaired SAR-to-optical image translation.
An Emotion Classification Scheme for English Text Using Natural Language Processing

With the development of Natural Language Processing (NLP), Artificial Intelligence (AI) has reached the level of understanding the context of long and complex sentences and interpreting the meaning. Since the AI model pre-trained with a large amount of data can produce high classification performance through a little fine-tuning, we aim to fine-tune and evaluate the two state-of-the-art pre-trained models with a dataset consisting of rich emotions to classify the various emotions. In order to show the potential, we evaluated two state-of-the-art models such as Bert [1] and Electra [2], and compared their performance in emotion classification.
A Study of real-Time 4K drone images visualization to rescue for missing people base on web

Recently, interest in the use of drones has increased, and drones are being actively introduced in various fields. We are trying to develop and use drones in various fields such as national defense, logistics, life safety, facility safety management, forest protection and monitoring, but there are still restrictions on the use of drones. The biggest limitation is the use of drones on land such as mountains and rivers. For example, if the police are searching for a missing person in an area with mountains, bushes, and a large river, multiple police personnel must visually check the drone footage on site every day. And then there's the problem of finding a missing person or lost article of a missing person and having to re-search where it was found. Therefore, the visualization technology proposed in this paper is a technology that visualizes real-time spatial mapping of drone images taken in real time onto a web-based 2D map. In cooperation with the missing person search AI inference function, the AI analysis result video is mapped on a web-based 2D map in real time. AI analysis results are visualized in real time on a web map using spatial information among the meta information in the video.
Survey on Advanced Video Streaming with Multi-Access Edge Computing

Video streaming market has been growing rapidly for the past two decades. YouTube and Netflix, for example, are part of everyday life for many of us. Although network bandwidth has increased with the development of 5G and fiber, user demand and expectations have also increased with 4K videos, larger screens, and COVID-19. Recent explosive increase in video traffic made it difficult for the network and applications to satisfy the user requirements of video streaming. Multi-access edge computing (MEC) is a promising technology to resolve this problem. MEC is a network architecture concept that offers cloud-computing capabilities and an IT service environment at the edge of the network. In this paper, we review the research efforts in video streaming with MEC over the past few years. We have clustered and categorized the literature according to the optimization criteria, with focus on cache management. We aim this survey to be a cornerstone for designing effective novel caching scheme in MEC for video streaming.
Crisis analysis on blockchain-based decentralized learning in wireless networks

The nodes may perform data training and make decisions according to the learning results for better performance in wireless networks. However, to guarantee privacy-preserving service, learning model parameters are exchanged among the nodes instead of exchanging the original raw data obtained at each node. Although the information exchange could be performed in a distributed manner by exploiting blockchain for verification, there still exist problems of jamming attacks in wireless channels. The jammers may interrupt transactions requests toward the normal nodes works as miners or try to corrupt the information. In this paper, we provide analysis results that blocks originated from attackers with jamming capabilities becomes main stream. Through numerical results, we show that attacks to normal miners are highly likely to be succeed by the attackers with jamming abilities even though the computing power sum of attackers is less than 51% in wireless networks.
Multi - Task Learning with Convolutional Neural Network for Packet Format Detection and Modulation Classification of Wireless LAN

In this paper, we propose multi-task learning (MTL) using a convolutional neural network (CNN) to simultaneously perform packet format detection and modulation classification of wireless LAN (WLAN) signals. Compared to single-task learning, the main advantage of this method is the high classification accuracy and rapid learning with a lightweight neural network model. These are achieved because of learning shared knowledge (i.e., model weight or gradients) between related tasks. This shared representation model increases data efficiency and reduces overfitting. The WLAN signals generated by the Matlab wave generator are used to validate and test the model. The results show that even with different time offsets, the proposed method has an accuracy of 98% for packet format detection and 86% for modulation classification.
Misbehavior Detection in Connected Vehicles using BurST-ADMA Dataset

The reliability and security of information within and between connected vehicles is now a burning research issue. Connected vehicle security is of utmost importance to military, civil, and civil-military operations. In addition, the vehicular network is a mission-critical system placing the demand for real-time and accurate detection on machine learning models. In this work, machine learning models were evaluated to guide in selection and design of reliable detection schemes. The novel dataset BurST-ADMA was used for the evaluation since it is an updated dataset with possible features for false message detection in IoV. Results show that optimizing ensemble models can enhance the accuracy and reduce mis-classification costs. The proposed AdaBoost outperformed other ensemble classifier models with an accuracy of 98.92 %.
GAN Inversion with Semantic Segmentation Map for Image Editing

In this paper, we propose a framework to perform Generative Adversarial Network (GAN) inversion using semantic segmentation map to invert input image into the GAN latent space. Generally, it is still difficult to invert semantic information of input image into GAN latent space. In particular, conventional GAN inversion methods usually suffer from inverting accurate semantic information such as shape of glasses and hairstyle. To this end, we propose a framework that uses the semantic segmentation map of the real image to guide the latent space corresponding to feature map with coarse resolution in the Style-GANv2. Experimental results show that our proposed method generates more accurate images and is possible of detailed editing of input images with a variety of semantic information compared with previous GAN inversion methods.
FaceBERT: Face De-Identification Using VQGAN and BERT

This paper presents FaceBERT, a face de-identification technique based on image generation to solve the infringement of portrait rights. Using the VQGAN model, it learns a quantized codebook that expresses an image in block units, encodes the image using the codebook, and then trains the BERT model. As a result of an experiment using FFHQ and CelebA-HQ, it was confirmed that the face images generated using FaceBERT were natural de-identified face images and different from the originals.
A Self-Learning French Language Learner Assistant Chatbot Leveraging Deep Learning

A chatbot is a virtual conversational assistant that is visible nowadays in many sectors in order to supplement the human workforce. The Internet is facilitating people who are enthusiastic about learning a new language. Learning a new language requires one to learn new words as well as a foreign grammatical structure, and this requires constant practice, which is difficult to do without having someone to practice with. To bridge this gap, we have proposed a chatbot based on a deep learning algorithm. This chatbot can be anyone's virtual assistant for having conversations in French or English (with the French meaning of an English sentence). In this chatbot, the user will not always get the same response to a particular question. Instead of this, users will get different responses to a particular question at different times, so that users can be introduced to many different French words. Self-learning is one of the highlights of this chatbot. This self-learning feature will be beneficial to increase the ability to give proper responses day by day to users by continuously having conversations with users. Even after not having a lot of data for training, this chatbot is able to provide outstanding performance due to having this feature.
Adaptive Loss Function Design Algorithm for Input Data Distribution in Autoencoder

The training performance of an autoencoder is significantly affected by its loss function. In order to improve the performance of autoencoders, it is important to design ap-propriate loss functions. However, many loss functions have been designed without taking into account the characteristics of the input data distribution. In this paper, we propose an algorithm for the design of a loss function by adaptively determining optimal parameters to input data distribution. Specifically, the proposed optimal parameters for loss function are determined by explicitly considering the dependency of the standard deviation of input data distribution. The simulation results confirm that the pro-posed algorithm can improve traditional loss functions. Moreover, the loss function determined by the proposed algorithm can reduce the computational complexity for training autoencoders.
SAR Image Despeckling Based on U-shaped Transformer from a Single Noisy Image

Remote sensing (RS) plays an important role in recent earth observation research and synthetic aperture radar (SAR) is widely applied as active remote sensing. Due to the coherent characteristics of radar illumination, the SAR images are polluted by speckle noise, which makes it difficult to understand and widely used. In this paper, we propose an improved SAR image despeckling approach based on a self-supervised strategy. The proposed method performs the transformer block and residual block as the main core of the architecture. We use the mean squared error with a regularization loss as the loss function. Experiments have been conducted on both simulated and real SAR datasets. The results show that the proposed method can preserve details and reduce smoothing better than several despeckling methods. The speckle noises were suppressed significantly, and the information of reconstructed images were well maintained.
Measurements of the Benefits of Edge Computing on Autonomous Driving

As the level of autonomous driving(AD) increases, the number of sensors in a single vehicle increases, and the required computing power also increases rapidly. The intelligence level of AD depends on algorithms and hardware performance. However, due to the problems such as limited production cost or computing resources, some vehicles may not have enough computation power for high level of AD. To overcome such obstacles, the distributed AD system architecture based on edge computing has become a trend. We build an edge computing-based distributed AD system on top of Robot Operating System(ROS2) and measure the performance with different settings. Our test results show the feasibility of AD systems centered on edge computing units, which open new research directions for exploiting edge computing in a more diverse way.
NU SAFE Faculty: A Microsoft PowerApps Application for Health Monitoring in National University Philippines

In the early stages of the COVID-19 pandemic, everyone is affected with changes in their lifestyle due to the enforcement of minimum public health standards and lockdowns. This study presents a web and mobile application with Microsoft PowerApps that will allow users in their health monitoring needs amidst the pandemic. The proposed web and mobile application will allow students and employees input their health declaration before entering the campus. The proposed system will also cover users to input their vaccination details and personal information for monitoring. Contact tracing through the mobile application is also proposed to assist the administrators in the early detection of confirmed positive cases.
TransTraffic: Predicting Network Traffic using Low Resource Data

In private 5G/6G networks, an adequate and accurate resource management is essential. In this paper, we propose a traffic prediction model, TransTraffic, that utilizes transfer learning for low resource data. Our evaluation demonstrates that leveraging prior knowledge from a similar traffic domain helps predict network traffic for a new domain or service.
3DFed: A Secure Federated Learning-based System for Fault Detection in 3D Printer Industry

This paper proposes a secure federated learning (FL) approach for 3D printer fault detection, namely 3DFed. Most current 3D fault detection systems were developed with a centralized learning approach, which is less efficient for large-scale deployment due to limited data for training. The FL-based system could be exploited to further increase fault detection accuracy while maintaining high performance by using the FedAvg algorithm. The 2D convolutional neural network (CNN) was used to extract data features from an image array. To further improve security in the simulation work, a certificate authority (CA) was added to maintain secure communication between the FL server and clients. The suggested 3DFed with the proposed CNN-based model can deliver high classification accuracy while preserving minimum time-cost, according to a thorough performance evaluation. Also covered in depth is how total client variance affects the learning process.
Adoption Block Chain Technology and Internet of Thing for Medical Record in Health Insurance

Insurance is an effort to transfer all risks that have not occurred, where the insurer guarantees all risks that may occur to the insured with the consequence that the insured pays a certain premium, health insurance is a type of insurance where the insurer guarantees all possibilities that occur to the insured. with health problems in accordance with the agreed agreement. Problems arise when the insured makes an insurance claim but does not follow the clauses in the insurance agreement. The difficulty of tracking the insured's activity which causes the rejection of the claim submission is one of the health insurance problems. This research aims to combine blockchain technology and Internet of Thing technology as a Health insurance claim solution. With a transparent blockchain all transactions that are part of the agreement on Health insurance can be accessed by all stakeholders accurately. With IoT technology, all insured physical activities that are part of the insurance agreement can be monitored in real time. The research method uses a qualitative approach with observations to identify problems and literature studies to find alternative solutions with an information technology approach.
RBCA-Net: Reverse Boundary Channel Attention Network for Kidney Tumor Segmentation in CT images

Recently, as the importance of early diagnosis and treatment of cancer has increased, many studies have been introduced to analyze medical images using deep learning. In medical image analysis task, the lesions segmentation methods uses a Fully Convolutional Network (FCN) architecture such as U-Net to predict the lesion area and play an auxiliary role in medical care. So many researchers are working on improving the performance of architectures. But, there are some challenges in that data is imbalanced and the size and shape of lesions are irregular. To solve these problems, we improved the segmentation performance by using a two-stage cascaded method. In stage 1, coarse region of interest (RoI) was extracted using ResUNet, In stage 2, we use Atrous Spatial Pyramid Pooling (ASPP) to extract features to contain a lot of spatial information using various receptive fields from a pretrained DenseNet-161 backbone. In addition, we introduce the RBCA module that combines Reverse, Boundary, and Channel Attention to capture various sizes and shapes of lesions. The performance of the proposed model shows high performance compared to various architectures using the KiTS19 dataset including kidney and tumor.
GCN-based Semantic Relation Network for Few-Shot Object Detection

Few-shot object detection has arisen to address the issue of data scarcity due to the long-tail distribution problem. The goal of few-shot object detection is to allow a pre-trained detector to generalize novel objects with few-shot data samples. However, few-shot object detectors misclassify novel objects into pre-trained base classes frequently. Existing methods applied a large margin classifier to address this problem, but they do not work well in data scarcity settings. In this paper, we propose graph convolution network-based semantic relation network to capture the semantic relations for few-shot object detection. Regardless of the amount of data, the semantic relations among object classes remain constant, which can support the classification of novel objects. Semantic relations are trained by the attention-based module adaptively to training data. Graph convolution network updates the object classifier using semantic relations. The object classifier is integrated and trained with the visual features. Through the comparison experiments with Two-stage Fine-tuning Approach (TFA), we observe that our proposed method achieves higher performance on few-shot settings than TFA.
Face Recognition and Identification Using Successive Subspace Learning for Human Resource Utilization Assessment

Attendance monitoring is an application of people counting with several use cases in various settings such as in corporate businesses and classrooms. Despite the vast number of possible applications, automated people counting or attendance monitoring applications in the Philippines are fairly uncommon. For example, establishments such as malls, supermarkets, terminals, and parks in the country still use manual counting. Their security personnel are given handheld counting devices to count the number of people entering the buildings or terminals. However, manual people counting, or attendance tracking methods are labor intensive, and thereby costly and time consuming. In spite of these issues, few establishments are interested in investing in automated counters as they tend to require devices or infrastructure (e.g., servers) which may be costly or impractical to scale. This paper aims to track a personnel's time management at work by monitoring his/ her dwell time in a dedicated workspace through face recognition. PixelHop algorithm was used to classify whether an individual is currently in their designated place, and to track the time they are within their workspace. This employs a successive subspace learning method which makes the face recognition more efficient with less overhead as compared to existing machine learning algorithms.