Reviewerly/exHarmony huggingface dataset:
A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean

In enterprises operating large-scale equipment, such as plants, maintenance workers must quickly and accurately find and understand the information in the equipment maintenance documents to perform maintenance tasks effectively. If the equipment maintenance documents include sentences with semantically ambiguous expressions, it will interfere with the maintenance knowledge search, and it may affect the maintenance performance of engineers. In order to solve these problems, text-based research of maintenance documents have been done to extract the key information or knowledge from these documents. Previous studies focused on finding the technical terminologies or calculating the similarity of documents using named entity recognition approaches. This paper proposes a method to extract knowledge of not only the technical terminologies but also their relations. The proposed method uses a rule-based approach that can be applied to the results of a named entity recognition approach and a dependency parsing approach. The named entity recognition approach found technical terms and the dependency parsing approach provided sentence structure information, so that the proposed method showed that a set of rules can extract maintenance knowledge, including entities and their relations. Trouble-shooting documents in the field were used as an experiment to demonstrate the effectiveness of the proposed method, and the experiment showed the possibility of practical use of the proposed method.
Online Reinforcement Learning Based HTTP Adaptive Streaming Scheme

DASH is an effective way to improve the Quality of Experience (QoE) in video streaming. However, most of the existing schemes depend on heuristic algorithms, and the learning-based methods that have recently started to appear also have a problem in that their performance deteriorates in a specific environment. In this paper, we propose an adaptive streaming scheme that utilizes online reinforcement learning. The proposed scheme adapts to changes in the client's environment by upgrading the ABR model while performing video streaming when QoE degradation is confirmed. In order to adapt the ABR model to the changing network environment, the neural network model is trained with the state-of-the-art reinforcement learning algorithm. The performance of the proposed scheme is evaluated through simulation-based experiments under various network conditions. Through the experimental results, it is confirmed that the proposed scheme shows better performance than the existing schemes.
Corridor Occupancy-Based Multi-Agent Pathfinding in Topological Maps

This study investigates the multi-agent pathfinding (MAPF) problem in a topological map consisting only of narrow corridors. When two or more robots move in a corridor, general MAPF algorithms determine the optimal corridor entry order to plan collision-free paths. However, depending on the width of a corridor, safety problems may occur if two or more robots navigate the corridor simultaneously. To ensure the safety of robot navigation, we propose a new MAPF method that prevents more than two robots from passing in the same corridor. This method plans multi-agent paths by assigning a spatialized constraint to a corridor entrance. The constraint allows only one robot to occupy one corridor.
Performance Analysis of Convolutional Neural Networks with Different Window Functions for Automatic Modulation Classification

Automatic modulation classification is one of the main tasks for developing information security and radio signal surveillance systems. It is also becoming increasingly significant for spectrum monitoring, management, and secure communications. Recently, deep learning models have been widely applied in many fields due to their outstanding feature extraction and classification accuracy. In this paper, the automatic modulation classification performance of several deep convolutional neural networks (CNN) is analyzed on the Fast Fourier Transform (FFT) based signal spectrum and the Short Time Fourier Transform (STFT) based signal spectrogram. By using ResNet18 and MobileNet cross-combined with FFT and STFT input data, the simulation shows that the STFT data provides a higher AMC accuracy than that of FFT data. On the same STFT data, the ResNet18 model outperforms three other models (SqueezeNet, GoogleNet, and MobileNet) in classifying 26 modulation types under the influence of five levels of fading noise with SNRs from −20 dB to +18 dB. Besides, the impact of different window functions in STFT is also investigated. Numerical results indicate that the considered window functions cause an insignificant difference in the AMC accuracy.
MCORANFed: Communication Efficient Federated Learning in Open RAN

To bring network intelligence closer to the end devices, Open Radio Access Networks (O-RAN) specifies a disaggregated and vendor agnostic framework of hierarchical processing units. Although this framework can be useful for certain use cases of 5G smart services, no standardised method to train Machine Learning (ML) models has been defined. Recently, Federated Learning (FL) has emerged as a promising solution for training in disaggregated systems. Unfortunately, the stringent deadline of O-RAN control loops and fluctuating network band-width poses challenges for FL implementation. In this paper, we tackle this problem by proposing an accelerated gradient descent method to expedite the FL convergence, and a compression operator to reduce the communication cost. We formulate a joint optimization problem to select the participating local trainers in each global round of FL and allocate the resources to these trainers while minimizing the overall learning time and resource costs. We design an FL algorithm (MCORANFed) which adheres to the deadline of O-RAN control loops. Extensive experimental results show that MCORANFed outperforms state-of-the-art FL methods such as MFL, FedAvg, and FedProx in terms of its convergence and objective costs.
A Survey on Security Threats in Blockchain-based Internet of Things Systems

The Blockchain-based Internet of Things (BIoT) systems are widely used in diverse industrial fields including agriculture, healthcare, sports business, and so on. However, since the Internet of Things (IoT) devices composing the BIoT systems typically have limited computation and communication resources, the IoT systems are likely to be exposed to various security threats. Lightweight blockchain can provide increased security, privacy, scalability and performance to the IoT systems. However, blockchain itself still has lots of security threats and vulnerability issues depending on its applications. Therefore, in-depth investigation of the security and potential vulnerability of blockchain is important to the secure BIoT systems. In this paper, we explore the security threats of the four layers of the BIoT systems, which is physical layer, network layer, blockchain layer and application layer. We then discuss its implication and future work in the BIoT security literature.
Parallel Execution of Solidity Smart Contract using Append-only Shared Log

Ethereum is one of the most actively used blockchain platforms that supports various decentralized services such as DeFi, NFT, and the Metaverse. Such services are enabled by Solidity smart contracts. A block is mined by executing a batch of transactions and reaching an agreement among consensus nodes. However, since the execution model of Ethereum is Order-Execute, all transactions must be ordered first and executed sequentially, resulting in limited block feed to consensus phase. This may lead to underutilization of consensus capacity. In this paper, we propose a parallel execution model for Solidity smart contracts using append-only shared logs. The parallel execution model consists of the following components: (1) key-value state sharding, (2) Solidity simulation module for each shard, (3) append-only log shared among Solidity simulation module; Append-only log stores the read-write set of a transaction in each entry, (4) the block mining module that is responsible for creating a block based on the shared log information and initiating the block consensus phase.
Influence of Household Tools in Regional Configuration of Earthquake Early Warning Systems

Tectonic activity can't be foreseen in time, therefore its occurrence causes every time human and economic losses. So far it has not been identified any solid method to forecast an earthquake instance. At the moment there are only methods for quick notification, send after the occurrence of the event, running the Earthquake Early Warning Systems (EEWS). Their main purpose is to disseminate the information about potentially catastrophic hazards as early is possible to vulnerable people in the targeted area, to provide enough time to implement automatized emergency measures. The warning is possible due to the fact that the onset of the earthquake is carried out by non-destructive primary P waves propagating with high speed. Performance of EEWS detection is given by technical characteristics and also by external natural factors, as changing sites’ configuration or parasitic signals that can interfere. In this context, the paper aims to demonstrate that there can be an overlap, an interval of confusion of the signals that EEWS can perceive and interpret as announcing natural catastrophes. There were tested eight heavy tools commonly used in household and their predominant frequency was calculated and compared to recorded seismic predominant frequencies. If the alert threshold level is set for 1Hz, it can trigger as well for heavy tools vibrations as for low intensity earthquakes.
LiveCap: Live Video Captioning with Sequential Encoding Network

Today, video captioning frameworks are very useful in places such as video surveillance systems. Most of these systems require real-time captioning, however existing video captioning frameworks still have some limitations in live video. Specifically, they require the whole video to describe. In this paper, we propose LiveCap, a framework for generating sentences corresponding to the current scene in real time from live video. LiveCap consists of three modules: sequential encoding network, captioning network, and context gating network. Our framework accumulates context for sequentially given video segments (sequential encoding network) and generates sentences based on it (captioning network). Furthermore, the context gating network controls the flow between the two networks to determine when to generate sentences. We train and test LiveCap on the ActivityNet Captions dataset and verify that LiveCap generates fluent and coherent captions in live video.
Investigation of unconditionally secured classical key distribution via a free space

The unconditionally secured classical key distribution (USCKD) has been presented for an air turbulent free-space optical link. Even in the noisy air channels, the USCKD has shown nearly unconditionally secure communications in data transmission owing to double unitary transformation in a round-trip scheme, where the absolute phase noises in both air channels are replaced by a relative phase noise between them. Unlike classical cryptography, USCKD is based on basis randomness and thus resistant to the attack of quantum computers. Unlike quantum key distributions, USCKD is free from nonclassical light requirement such as a single photon, which is sensitive to quantum loopholes of channel loss and imperfect detectors.
Comparison of optical flow image preprocessing options for state of the art deep learning models

In recent years, many new deep learning approaches for solving optical flow estimation have been showing impressive results. But as model sizes have been becoming larger, executing them became a task that requires expensive, high end, hardware. Because most of these models use full colour RGB image pairs as an input, we test several image preprocessing methods with the goal of finding techniques that could alleviate some of these inefficiencies. We conducted these experiments using the state of the art GMA (Global Motion Aggregation) network architecture. Our results, first of all, show that optical flow can be estimated equally well with single channel greyscale images, this finding could be used to lower model sizes in general. We also find that calculating the derivative of an image in the direction of one of its axes leads to improvements in accuracy, but only in the case of less difficult optical flow data sets such as FlyingChairs and SIntel-clean.
Deep Learning Based Human Activity Recognition With Improved Accuracy

Perceiving human exercises from video clips or still pictures is a provoking mission because of issues like, changes in scale, perspective, lighting, and appearance of source images. Human action acknowledgment is a difficult time series order task. It includes anticipating the action of an individual in light of image sensor information and generally requires profound area mastery and techniques of image processing to accurately extract meaningful feature data from the crude information to fit an artificial intelligence model. Currently available models are exceptionally tedious and lack accuracy of classification result. So there is a need to plan a Human action acknowledgment model which can be accurate and can be utilized efficiently in present world applications. This model will not just be practical yet in addition will be a utility-based model that can be utilized in an enormous number of applications such as observing and caring home alone elderly people or monitoring any unattended patient in a hospital. In this proposed model, source video dataset is wisely prepared for a meaningful and concise feature extraction by techniques like optical flow and 2D spatial temporal feature extraction. Then, these features are fed to the model for training by a VGG-19 Algorithm to effectively increase the accuracy of the Human Activity Recognition model compared to the existing system.
Estimating Deep Curve and Illumination Maps for Old Image Brightness Enhancement

Brightness enhancement aims to enhance the brightness of dark or low-light images in order to improve quality and visibility. Numerous research studies have been carried out to increase the brightness of low-light images. However, the majority of the study focuses on modern high-quality low-light images only. Old photos are dark, noisy, and degraded compared to normal modern images. Using state-of-the-art networks trained on modern low-light images to improve the brightness of old images leads in color-distorted, overexposed output due to the characteristics of old images. In this paper, a deep convolutional neural network is proposed to perform the brightness enhancement of old photos. The proposed network is a combination of an illumination network and a deep network that estimates best-fitting light enhancement curves for a given input image. The combination of the illumination network and curve estimation network do better brightness and color enhancement resulting in more natural output. According to experimental findings, the suggested technique works better than the conventional methods for brightness enhancement on old image datasets in terms of visual quality.
Federated Codebook for Multi - User Deep Source Coding

This paper investigates Deep Learning based Source Coding (DeepS C) with multiple users. While most of the existing works focus on a single pair of DeepSC transceivers, we consider multiple pairs of transceivers, each of which is modeled as a Vector Quantized Variational Autoencoder (VQ-VAE) architecture. Furthermore, in contrast to existing DeepSC works exploiting the trainability of encoders and decoders, in this work we focus on the trainability of codebooks. Inspired from this and Federated Learning (FL), we propose a novel DeepSC framework with federated codebook (FC- DeepSC) wherein each transceiver iteratively exchanges their codebooks during training, so as to construct an averaged global codebook that is downloaded by each transceiver. Simulation results corroborate that FC- DeepSC achieves faster convergence than DeepSC.
Continuous Differential Image-based Fast Convolution for Convolutional Neural Networks

Convolutional neural networks with powerful visual image analysis of deep structures are gaining popularity in many research fields. The main difference in convolutional neural networks compared to other artificial neural networks is the addition of many convolutional layers. The convolutional layer improves the performance of artificial neural networks by extracting feature maps required for image classification. However, for applications that require very low-latency on limited processing resources, the success of a convolutional neural network depends on how fast we can compute. In this paper, we propose a novel convolution technique of fast algorithms for convolutional neural networks using continuous differential images. The proposed method improves the response speed of the algorithm by reducing the computational complexity of the convolutional layer. It is compatible with all types of convolutional neural networks, and the lower the difference in the continuous images, the better the performance. We use the darknet network to benchmark the CPU implementation of our algorithm and show state-of-the-art throughput at pixel difference thresholds from 0 to 25 pixels.
Lightweight Signature-based Range Proof

Our proposed scheme is modified from that of Camenisch et al., yet achieves more efficient than their scheme. In general, a batch verification method is more advantageous and is also able to save more cost than the original scheme, while non-interactive zero knowledge proof requires fewer communication rounds than interactive zero knowledge proof. The proposed scheme employs the simplified small exponents test method for secure and fast batch verification on multiple signature elements, which can remarkably reduce the number of time-consuming pairing operations required by both a prover and a verifier, resulting in greatly improved computational efficiency. By applying non-interactive zero knowledge proof technique, the proposed scheme also decreases the number of communication rounds required between both of them.
A Framework to Combat COVID19 like Pandemic in Future

This paper outlines a framework to prevent the COVID19 like pandemics for visitors to buildings or sites that receive many visitors. The proposed system is used to detect visitors who have not worn a facemask, or visitors with high body temperature, communicate daily visitor data to the security officer, sound an alarm to notify the officer, and screen the visitors with the results of the measurements. Also, the proposed solution uses deep learning and computer vision techniques to detect the facemask. Further, a testbed is designed based on an Arduino microcontroller connected to a PC for collecting, processing, and storing the data. Furthermore, the proposed system used a contactless infrared temperature sensor to avoid any chance to transfer the COVID-like disease to normal visitors. Finally, we tested the system by passing many subjects with and without face masks and high temperatures. The accuracy of the system shows that the system accurately detects each subject with and without a face mask and with high temperatures.
PyMatching-based Decoding Simulation of Rotated Surface Codes in Classical Quantum Simulator

PyMatching provides a method for decoding quantum errors using the minimum-weight perfect matching algorithm. However, although the PyMatching supports various quantum error correction codes, no deep study has been conducted on the rotated planar surface codes that are receiving the latest attention. In this study, we define a procedure for detecting errors using PyMatching for rotated planar surface codes and analyze the correctness of error correction results. To compare error correction behavior by logical qubit size, we used logical qubit with distances of 3, 5, and 7, respectively. Furthermore, we have conducted comparative empirical studies by comparing the theoretical predictions of PyMatching with the actual execution results of classical state vector simulators.
Reducing Iterations of Grover Search Algorithm for N-Queen Problem U sing Quantum Permutation States

In this paper, we propose a construction method of Grover's algorithm to solve the N-Queen problem. Quantum permutation state was designed and applied to the initialization and amplitude amplification process in Grover's algorithm. An oracle-level quantum circuit was constructed using Boolean algebraic expressions. We reduced the number of iterations of the Grover's algorithm by decreasing the number of superposed inputs in the initialize step using quantum permutation state. We show that our algorithm has less time complexity compared to previous study that solved the N -Queen problem using Grover's algorithm with W state as a input.
Blockchain-Based Trusted Container Orchestration for Edge Computing

In recent years, IoT elements are growing exponentially. The massive amount of data generated by these devices poses a daunting challenge for sensors and devices of limited capability and resources. Edge computing is considered as a promising solution to provide efficient data processing by bringing the computation capability closer to the network edge. However, edge computing vision faces several challenges in terms of security and application orchestration. This paper proposes a trusted containerized application orchestration framework by leveraging blockchain technology. The proposed approach is based on the Ethereum blockchain and Kubernetes container platforms to integrate access control and application orchestration in an edge networking environment. We evaluate the proposed approach through a proof-of-concept prototype implementation along with trusted orchestration operations. The result of our simulation study shows the effectiveness of the proposed approach.
Image Steganography with Deep Learning Networks

Currently there are many ways to achieve information hiding in images. Each image steganography method works to increase the payload capacity while hiding the secret information in the cover image in an undetectable way, and then the receiver is able to use the extraction network to perfectly recover the secret information from the stego image. In this paper, we explore how three different network structures (convolutional neural network structure, U-Net structure, and Swin Transformer structure) solve the image embedding and extraction problem. We use the same dataset to validate the three network structures and visualize the process and effectiveness of the three network structures in achieving image steganography from different experimental results, in addition to using the peak signal-to-noise ratio (PSNR) and structural similarity between images (SSIM) to measure the image quality.
Application of Recommendation System for Personalized Smart Home Care

Smart home care refers to a technology used to provide optimal life care services through information on the state of users in the living space and surrounding environment. With the recent rapid development of online platforms, many companies have begun to focus on e-commerce, making the development of recommendation systems. We intend to propose personalized smart home healthcare and smart home beauty care to users by applying a personalized recommendation system to smart home care.