Reviewerly/exHarmony huggingface dataset:
Study on the Vulnerability of Video Retargeting Method for Generated Videos by Deep Learning Model

Text-to-video generation is getting attention and the generated videos can be used in many applications. However, it is uncertain whether existing deep learning techniques work well for generated videos. In this paper, we compose a study of how generated videos can be retargeted by deep learning models with the ratio of the main object preserved and looked for ways to improve the quality of the generated and retargeted video frames. Throughout the experiment, we discover the errors of video retargeting on the generated videos in the processes of segmentation, inpainting, and relocating.
Towards Enhancing Cyber Security Awareness Using Gamification Escape Room

The cyber-attacks are becoming more complex and prevalent, therefore, there is a need for raising cyber security awareness to educate and defend people about the latest hacking attacks by using creative and efficient education methods. Similarly, gamification escape rooms have acquired popularity as a cutting-edge and efficient way to increase cybersecurity awareness and individuals’ abilities to identify, avoid, and respond to cyber risks. The proposed approach includes developing a cybersecurity escape room on a website or application that uses gamification and puzzle-solving tasks to mimic actual cyber-attack scenarios. The cybersecurity escape room offers a virtual setting where users can practice cybersecurity techniques and learn to prevent potential security attacks from attackers, creating a secure and educational environment where users can gain experience and learn the skills, they need to deal with real-life hacking situations. By using this method, users can have fun and experience the adventure of a cyber threat scenario while making informed decisions and raising their security awareness.
A Robust Aggregation Approach for Heterogeneous Federated Learning

Federated learning is a cutting-edge method of model training, which leverages the end users to train the global model on the server. The end users are responsible for training locally on their datasets and update the shared global model. Once the local training is executed, the local trained models are forwarded back to the server to further upgrade the global model by performing aggregation. This process of global training is carried out for certain number of rounds. Practically, the datasets of clients are distributed heterogeneously. Thus, the updated local models by clients emanate broad variation among local models due to heterogeneity. In other words, the aggregation of local models plays a vital role in federated learning. Specifically, aggregating the diversified local models may deliver unsatisfactory output if not performed efficiently. This article presents a performance efficient and robust aggregation approach for heterogeneous federated learning called FedLbl. Our approach takes the diversity of data among clients into consideration before conducting the aggregation of local models. Our study compares the proposed method with conventional federated learning techniques, resulting in a 28% increase in accuracy and a 19% reduction in loss.
Two-channel Image Dehazing Algorithm Based on the Improved Guided Filter for Intelligent Automobile

This work proposes a two-channel image dehazing strategy based on threshold segmentation. The algorithm first uses the Otsu threshold method to segment the sky and the background, and then estimates the dark channel and bright channel atmospheric light values of the sky and background respectively, and uses a weighting function to fuse them, and finally obtains the accurate mixed atmospheric light of each area. In order to improve the detail effect of the restored image, an improved guided filtering technique is proposed to further process the obtained transmission image. Experiment tests show the performance of the proposed method.
Uplink Time Constrained Federated Learning over Wireless Networks

Federated learning (FL) is an emerging distributed learning paradigm that collaboratively trains a shared model while preserving their data privacy. However, clients participating in the FL process have heterogeneous computation power and communication resources. Therefore, the central server needs to wait for the slowest client finishes uploading its local model update, which is known as straggler effect. In this paper, we propose a novel FL method with uplink time constraint set by the server to mitigate the straggler effect over wireless networks. First, we study the impact of uplink time constraint on the number of clients with successful upload in Rayleigh fading channel. Since acquiring perfect channel state information (CSI) is challenging in practical FL applications, the server exploits channel statistics instead. We then formulate an optimization problem for the error convergence speed with respect to uplink time constraint. Finally, we confirm that the proposed scheme, namely FL with Uplink Time Constraint (FedUTC), achieves faster convergence speed than the baseline FL method.
Real-Time Pill Identification with Prescription Confirmation Using Deep Learning on Embedded System

In the study, a real-time pill identification with prescription confirmation within medical packaging using deep learning on embedded system is proposed. In this system, multiple pills within a medical packaging for inpatients can be identified using deep learning and confirmed with the doctor’s prescription in QR code to avoid error in the packaging process. The system is implemented in an embedded system with GPU for accelerating the identification and confirmation processes. Experimental results of the proposed system using Nvidia’s Jetson Nano shown that multiple pills with different color and shapes can be identified and confirmed with doctor’s prescription in real time.
Evaluation of Neural Demappers for Trainable Constellation in an End-to-End Communication System

Conventional M-ary Quadrature Amplitude Modulation (M-QAM) constellation designs such as rectangular constellation, are based on mathematical data and estimated channel models to achieve equal probability of error for transmitted bits in communication systems. However, these designs are suboptimal as they are not receptive to practical channel conditions or system performance due to their fixed lattice structure, and their performance degrades with a higher number of bits per symbol. Deep learning (DL) based end-to-end communication systems can be utilized to circumvent these challenges for better overall performance. Such systems are implemented as deep neural network (DNN) autoencoders, where trainable constellations and neural demappers (ND) can be jointly trained to achieve optimum constellation design for a higher data rate communication system. In this study, we evaluate the performance of two NDs that implement trainable constellation design and compared them with two baseline demapping algorithms in an end-to-end communication system. In the analysis, the NDs outperformed the baseline demappers for higher bits per symbol transmission, and trainable constellation design corresponding to 1024-QAM achieved the highest gain of 0.8 dB compared to the baseline demappers.
Smart Application for Real Time Detection: An Improved Lightweight Detector for Real-Time Vehicle Detection

With the goal of creating a model that satisfies the demands of detecting vehicles on the road in real time and delivers high FPS speed and high accuracy, we propose enhancements to the YOLOv4-tiny detector in this work. We suggest a modification to the FPN to increase the semantic and location information between the higher and lower levels. Also, to further solve the issue with NMS, we employed soft-NMS in our model. Moreover, to address the drawbacks of the IoU, we proposed a way to improve anchor clustering. The experimental results show that our proposed model outperforms the basic model with 3.29% higher mAP and 1.17 higher FPS.
A Study of Joint Framework for Robustness Against Noise on Speaker Verification System

As speaker verification (SV) technology is applied to various fields with the development of deep learning, robustness against noise is becoming more prominent. A common approach for suppressing the noise effect is to jointly train SV system along with a front-end enhancement module as pre-processing. However, in this paper, we explore a training method for a noise-robust system that focuses on enhancing the single SV system. In particular, we introduce the feature-robust loss to exploit the embeddings extracted from the pre-trained model, which is jointly trained with the enhancement module. Experimental results showed that our proposed method can mitigate the noise effect in various noisy conditions using only a single SV system, demonstrating its potential for further development.
Adversarial Attacks on Deep Neural Networks using Generative Perturbation Networks

A new deep learning (DL) network called Generative Perturbation Network (GPN) is proposed. The GPNs are capable of learning to modify inputs to deep neural network (DNN) models trained to classify images in imperceptible ways leading to misclassification. Unlike previous approaches to generate such adversarial samples to DNN classifiers, the proposed GPN does not need to know the architecture of the neural network that it is trying to attack nor have access to the data used to trained it. It is shown that with just being able to observe the final output labels from the trained classifier to any given input image, the GPNs are able to learn to minimally perturb the images to achieve misclassification. Simulation results show that a proposed GPN can easily degrade the 98.65% accuracy of a trained CNN on the MNIST hand-written digit dataset down to about 52%. Simulation results also show that different regularizations can be used in the generator loss function to achieve various visually desirable characteristics in the generated adversarially perturbed images.
An Efficient Microservices Architecture for MLOps

In a microservices architecture, each service has a database. Hence, it is important to communicate and synchronize data between services. The SAGA pattern is a traditional microservices architecture pattern, and the command query responsibility segregation (CQRS) pattern has recently attracted increasing attention. Machine learning model operation management (MLOps) aims to stably deploy and maintain the system by preprocessing big data and learning machine learning models. Data processing in the microservices architecture is important because considerable data is used. This paper proposes an appropriate architecture for each microservice to perform efficiently in the MLOps environment.
Traffic Accident Detection and Classification in Videos based on Deep Network Features

The number of vehicles on the road has increased significantly, which pose a number of challenges to cope with for traffic management. Especially road accidents need instant attention to reduce the loss of people life and their property. In this paper, we propose a traffic accident detection and classification framework, which automatically detects accident in traffic videos using deep networks features and also classify that accident into car-car and car-bike collisions. The proposed framework works in two phases. Accident anomaly detection: We explore three convolution neural networks (CNN’s) named GoogLeNet, AlexNet and VGGNet, where deep features are extracted using these networks and a one class support vector machine (OCSVM) is trained on each network deep features, which are used to detect accident anomalies in a outlier fashion. Accident anomaly classification: where a multi-class SVM model is trained using the features of the detected accident frames and is used to classify accident into car-car and car-bike collisions. The experimental results on UCF-Crime road accident video sequences show that the proposed approach achieves high accuracy on both traffic accident anomaly detection and classification.
Deep Learning Image Analysis System on Embedded Platform

Recent advances in the field of deep learning technologies have made it possible to develop practical video analysis systems with embedded platform that are more accurate and faster than prior embedded systems based on pattern analysis technology. In video analysis applications, object detection, face recognition, action recognition and super resolution technology is the most important functions. In this paper, we show action recognition and super resolution on embedded deep learning system. It is discovered that ResNet34 structure with 16 frames analysis is most profitable for speed and accuracy and Attention based super resolution method is enough for real-time processing. Both deep learning models optimized for speed and accuracy are operated on embedded system with Intel NPU at real-time. We introduce main technologies in chapter 1. Proposed method is shown in chapter 2 and the result is shown in chapter 3.
Implementation of Danger Degree Calculation System for Public Safety Services

Danger degree calculations are important to provide public safety services because crime response methods vary depending on danger degrees of crime scenes. This paper proposes a danger degree calculation system (DDCS) to calculate the danger degrees by using natural language processing (NLP) of emergency report data that summarize emergency calls. This paper also explains an implementation of DDCS by building a deep learning NLP model with KoBERT and visualizing a process of danger degree calculations. Danger degrees from the DDCS will help a constabulary to determine appropriate physical forces (e.g., devices and crime control actions) for police officers’ responding to the crime scenes.
Structured Medical Dataset Analysis Tool Based on ChatGPT

This study proposes a medical data analysis tool using Chat Generative Pre-trained Transformer (ChatGPT), a natural language processing model (NLP), to directly communicate with medical data for various analyses. The tool recommends optimal AI models in terms of accuracy and efficiency using Shapley Additive explanation (SHAP) and showed average accuracies of 90.6% and 82% in the data analysis and AI model analysis stages, respectively. The proposed method showed the potential of chatGPT to revolutionize medical data analysis.
Development of Edge Camera System for Vehicle Detection System Using Local AI Optimizer Based on Minimum Network Resource

This paper proposes an edge camera system for a vehicle detection system using AI local optimization method utilizing minimal network transmission data. Currently, various AI CCTVs are installed, but if they are installed in an area without data network support, updates are slow and optimization is difficult. We improve traffic object recognition by remotely optimizing the detector with minimal data in a 3G or so communication environment, and use it to estimate the speed and location of the vehicle. Local AI optimizer utilizes optimized weight data using DBs using environmental data-based background images, and vehicle speed estimation utilizes warping data-based tracking data. We confirmed the high sensing performance and speed recognition rate through certification exam of the proposed edge camera system.
A novel LLR scaling factor selection using LDL Bayes classifier

In this paper, we suggest a novel learning algorithm that determines a scaling factor to adjust the log likelihood ratio (LLR) extracted from the symbol detector. The LLR scaling factor to overcome the constraints of HW can affect the performance depending on the determined value. It has the ambiguity that selects the value of LLR scaling factor since determining the value would be changed rapidly under the small difference of environment. On the other hand, among machine learning types, label distribution learning (LDL) is known to be beneficial for learning ambiguous data because it distinguishes the differences regarding each label as the distribution. After the proposed learning algorithm using LDL Bayes classifier learns the ambiguity of each scaling factor, the learned Bayes classifier determines the LLR scaling factor offering higher performance. As a result of the simulation, the proposed learning algorithm provides improved performance compared to the existing fixed LLR scaling factor.
On-Device Deep Learning-based Multiple Behavior Detection using IMU Motion Sensors

This study proposes a system for monitoring the behavior of patients using an on-device deep learning-based inertial measurement unit (IMU) motion sensor. The wearable device captures the patient’s four active behavior states (walking, eating, falling, and resting) using a three-dimensional accelerometer (ACC) and gyroscope (GYR). Five features, including mean value, standard deviation, median absolute deviation, minimum, and maximum, are applied to each 1- second segmented sample to extract the most significant characteristics from the signals. Four machine-learning approaches, such as support vector machines (SVM), multilayer perceptron neural network (MLP), long short-term memory (LSTM), and convolutional neural networks (CNNs), are used to evaluate the system’s viability for different patient behavior identifications. The CNN algorithm showed the highest accuracy in patient behavior classification, surpassing the other algorithms by 92.68%. This algorithm is installed directly on the wearable device due to its exceptional performance, increasing system efficiency, and decreasing data transmission and connection latency. Additionally, a software program installed on the computer helps obtain necessary data from the wearable device through Bluetooth. It enables doctors, nurses, or supervisors to monitor a patient’s behavior and other relevant information. The study’s analysis results demonstrate the reliability of the device-based deep learning system for patient behavior recognition.
A Study on Latency Prediction in 5G network

These days, due to the increase in the use of mobile terminals such as smartphones, tablets, and XRM(Extended Reality and Media) service terminals, heterogeneous networks for various services are often connected to the 5G network. Low latency should be supported on the network for these services. At the time of measuring the latency at the current time point, recalculating the end-to-end QoS path, or informing the XRM service application, it can be a past value, which can lead to an inaccurate situation. To overcome this situation, 5G network needs to predict latency in advance, recalculate end-to-end QoS paths based on this information, or informs XRM applications to meet more effective QoS requirements. In this paper, we have evaluated the performance of several machine learning models for predicting latency, and introduce the results of experimenting with performance.
Federated Learning Using Blockchain-based Marketplace

Federated Learning (FL) is an emerging machine learning paradigm aimed at collaboratively training AI models with distributed multiple clients in a privacy-preserving manner. However, the performance of FL can be poor, especially in cross-silos FL, without sufficient participants who train models with their data. Therefore, finding relevant datasets and engaging their owners in FL can be one of the key important factors in reality. Motivated by this, in this paper, we introduce a system equipped with efficient ways to search for data owners who possess suitable datasets and to inspire them for cooperation. In our approach, all participants who perform the learning process in FL can receive transparent and automated incentives from every gain of the final output, which can work as a motivation for participation in federated learning.
Extended U-Net for Satellite Image Semantic Segmentation

In this paper, we propose an extended model of encoder-decoder structure using U-Net to improve accuracy in satellite image semantic segmentation. The U-Net is a deep learning-based semantic segmentation scheme, and research is being conducted in various applications such as road sign detection, medical image analysis, and tumor detection. The conventional U-Net suffers from losses during feature compression-expansion due to shallow structures of encoder-decoder. This creates a problem of reduced segmentation accuracy. In order to address this issue, the proposed scheme exploits an extended structure using concatenate upsampling and residual learning, which remedies the loss of information and improves the accuracy of semantic segmentation. In the experiments, segmentation was performed on various satellite images, and it was shown that the proposed U-Net was superior to the conventional counterpart.
Indoor Scene Recognition Using ARM-based MobileNets Architectures

The rapid development of science and technology has improved the quality of people life. In recent years, the use of microcontrollers has increased due to the rise of edge computing. Based on low cost, low power consumption, and high stability, the controller can be widely used in various fields. In this research, an ARM-based platform is applied with a camera module to perform image recognition tasks. The MQTT protocol is realized to transmit the image recognition results. The MobileNets models are developed with X-CUBE-AI tool to perform transfer learning on indoor scene datasets. The verification results are obtained by training MobileNetV1 and MobileNetV2 structures. The proposed image system indeed achieves the average accuracies of 67.2% and 71.6% for MobileNetV1 and MobileNetV2, respectively.
A Solar Tracking System Using Feedback Controller and State Estimation Filter

In this paper, a solar tracking system with feedback controller and state estimation filter is designed with consideration of unpredictable disturbance and feedback sensor noise and verified through various computer simulations. It is verified that the designed solar tracking system with PI controller and Kalman filter has the ability to reject disturbance and reduce feedback sensor noise.
CTC: Content-Aware Tailoring of Adaptive Video Streaming using Multi-Head Critic Network

In this paper, we aim to enhance video streaming quality by taking into account a simple observation: users tend to focus on specific areas within a video. For instance, low-quality or stall events during scoring moments in sports videos can lead to user frustration. However, most existing video streaming solutions treat all scenes equally. In our work, we introduce CTC, an ABR algorithm that adjusts its policy based on scenes. To achieve this, we first model dynamic QoE based on scenes and then use reinforcement learning to adapt the policy in real-time. As a result, CTC significantly improves QoE by adjusting its policy according to content compared to existing work.
Efficient Throughput Degradation Prediction in Telco Networks using Anomaly Detection

Proactive anomaly detection of the Key Performance Indicators (KPIs) in a telecom network is important for consistent end-user experience. In this paper, we present an application of state-of-the-art deep learning based multivariate time series Long Short Term Memory (LSTM) model which can forecast KPIs based on historical data. Our approach is able to efficiently predict KPIs by learning patterns from the time series data along with the seasonality behaviour. We also predict anomalies on this forecasted data by the application of Isolation Forest, that is tuned with the contamination hyperparameter to indicates the severity of the anomalies. We evaluate our approach on real data from a LTE network and observed good results for forecasting the throughput KPI and predicting anomalies.
Various Object Trackers for UAV Chasing of Counter UAV Systems

In this study, we examine eight different object trackers for UAV chasing. The chasing algorithm employs version three of you only look once version (YOLOv3) and the considered object tracker. Following an object tracking benchmark, success rate, precision, and complexity were compared. From the numerical results, it was shown that the proposed UAV chasing algorithm with a tracker achieves improved chasing performance with less computational complexity.