Reviewerly/exHarmony huggingface dataset:
Proposal of Descriptive Approach for XR Device Sensor Data Handling and Compatibility

This paper presents an approach to address the challenges of handling sensor data and compatibility issues arising from the rapid advancement of Extended Reality (XR) technology and the diverse range of XR devices in the market. In order to overcome the diversity and development challenges of XR devices, the concept of device profiling is introduced. This aims to provide XR application developers with a standardized method to define and manage sensor data for each device, thus offering convenience.An XR device profile consists of a device ID and sensor properties, where the latter includes sensor ID, type, data format, and characteristics. This information is expressed in JSON format, which offers various advantages, such as maintaining data structure consistency, hierarchical data representation, resolving language dependencies, readability, and ease of maintenance and customization. This approach of device profiling proposed in this paper offers a consistent and flexible way to handle the diverse sensor data of XR devices, thereby reducing developers' challenges and addressing compatibility issues. Furthermore, this profiling approach is expected to promote unity within the XR ecosystem and facilitate faster development and innovation in XR technology. We anticipate that future research and collaboration will further enhance sensor data management in XR devices and improve the developer experience.
Vitexco: Exemplar-based Video Colorization using Vision Transformer

In the field of image and video colorization, the existing research employs a CNN to extract information from each video frame. However, due to the local nature of a kernel, it is challenging for CNN to capture the relationships between each pixel and others in an image, leading to inaccurate colorization. To solve this issue, we introduce an end-to-end network called Vitexco for colorizing videos. Vitexco utilizes the power of the Vision Transformer (ViT) to capture the relationships among all pixels in a frame with each other, providing a more effective method for colorizing video frames. We evaluate our approach on DAVIS datasets and demonstrate that it outperforms the state-of-the-art methods regarding color accuracy and visual quality. Our findings suggest that using a ViT can significantly enhance the performance of video colorization.
AuPPLE: Augmented Physical Priors through Language Enhancement using Self-Supervised Learning

In recent years, a contentious debate has emerged surrounding the degree to which Large Language Models (LLMs) can truly achieve grounding in the physical world. Grounding, in this context, refers to the models’ ability to establish a meaningful connection between their language-based understanding and a concrete comprehension of real-world phenomena. Our research aims to explore the latent capability of LLMs to develop physical intuition: a prerequisite for embodied agents to effectively perform tasks in real-world environments. In this paper, we release a novel dataset of physical scenarios that serve as a benchmark for an LLMs’ physical intuition. Our benchmark AuPPLE (Augmented Physical Priors through Language Enhancement) for Language Models includes scenarios regarding free-fall and projectile motion, including various question-answer formulations: MultiQA, binary classification, and continuous number prediction to comprehend linguistic nuances and apply their understanding within a physical context. By meticulously fine-tuning LLMs on this specialized dataset, we assess their performance in providing responses that showcase an ability to draw upon underlying physical knowledge. With our fine-tuned LLMs achieving over 87%—more than 3 times its base model—on free-fall evaluation dataset, our results shed light on the intrinsic grounding capabilities of LLMs, offering insights into their potential to bridge the gap between language and the physical world. This paper contributes to the ongoing discourse on the true nature of LLMs’ comprehension and its relationship with real-world context, underscoring the strides made in enhancing their intuitive understanding through targeted fine-tuning techniques.
Language-Model-based methods for Vietnamese Single-Document Extractive Summarization

Latterly, there has been a dramatic increase in the amount of text data which demands effective summarization. This paper proposes a method of using English text summarization frameworks and Vietnamese pre-trained language models for Vietnamese single-document extractive summarization. The experiments were conducted with three frameworks namely BertSumExt, MatchSum, and CoLoExt, and two pre-trained language models namely PhoBERT and BartPho. Our models are evaluated on two well-known Vietnamese summarization benchmark datasets, namely Vietnews and Wikilingua, and achieved state-of-the-art results on Vietnews with a maximum ROUGE-1/2/L score are 57.15/26.23/39.76. The results on Wikilingua also show the effectiveness of our methods.
Human Detection in Infrared Image Using Daytime Model-Based Transfer Learning for Military Surveillance System

In the field of civil and military surveillance, there has been a gradual increase in the application of deep learning-based image object detection systems to enhance accuracy. During daylight hours, the desired objects can easily be detected using cameras combined with image processing or deep learning techniques. However, during nighttime or when weather conditions prevent daylight, detecting objects through regular RGB cameras becomes challenging [1]. This paper proposes a method for detecting humans using images captured from infrared cameras, aiming to facilitate whole day surveillance systems for military bases. Typically, public image datasets almost consist of RGB images taken during the day [2]. However, gathering infrared image datasets is both essential and labor-intensive. Therefore, to minimize the collection of such infrared datasets, this study suggests using transfer learning based on daytime models to detect humans in infrared images. With a limited amount of collected infrared data, this transfer learning approach can enhance the accuracy of detection.
Prompt-Based Segmentation and Inpainting : A New Approach to Disaster Image Creation

This paper proposes a novel approach to disaster image generation using prompt-based segmentation techniques. By segmenting terrains based on the provided prompt and inputting disaster-related prompts into the segmented area, we explore a method to generate images that reflect disaster scenarios. Moreover, we propose a method to adjust the inpainting mask area according to the severity of the disaster, providing a visual representation that varies with the situation. We acknowledge limitations in areas such as the inpainting mask region and the overall disaster image generation, and suggest directions for further research to overcome these challenges. We emphasize that the methodology of our study contributes significantly to disaster management and information dissemination.
StyleBoost: A Study of Personalizing Text-to-Image Generation in Any Style using DreamBooth

Recent advancements in text-to-image models, such as Stable Diffusion, have demonstrated their ability to synthesize visual images through natural language prompts. One approach of personalizing text-to-image models, exemplified by Dream-Booth, fine-tunes the pre-trained model by binding unique text identifiers with a few images of a specific subject. Although existing fine-tuning methods have demonstrated competence in rendering images asking to the styles of famous painters, it is still challenging to learn to produce images encapsulating distinct art styles due to abstract and broad visual perceptions of stylistic attributes such as lines, shapes, textures, and colors. In this paper, we present a new fine-tuning method, called StyleBoost, that equips pre-trained text-to-image models to produce diverse images in specified styles from text prompts. By leveraging around 15 to 20 images of StyleRef and Aux images each, our approach establishes a foundational binding of a unique token identifier with a broad realm of the target style, where the Aux images is carefully selected to strengthen the binding. This dual-binding strategy grasps the essential concept of art styles and accelerates learning of diverse and comprehensive attributes of the target style. Experimental evaluation conducted on three distinct styles - realism art, SureB art, and anime - demonstrates substantial improvements in both the quality of generated images and the perceptual fidelity metrics, such as FID and CLIP scores.
A Reduced Complexity Method of Recursive Quantum Approximate Optimization Algorithm

Among quantum algorithms, the quantum approximation optimization algorithm (QAOA) is an algorithm that finds approximate solutions. Recursive QAOA is proposed to overcome the limitation that QAOA has lower performance than Goemans-Williamson algorithm in the MAX-CUT problem [Physical Review Letter, 125, 260504, 2020]. In this paper, we introduce a method with lower complexity than RQAOA and compare its numerical results in the MAX-CUT problem. At the same level, the performance of the proposed method is lower than that of RQAOA, but the number of computations of quantum and classical parts is very small.
Code Repository Vulnerability Focusing on RepoJacking

A supply chain attack is a type of cyberattack executed by infiltrating a target system during software distribution, allowing malicious code disguised as something benign to be distributed without raising suspicion. Any supply chain attack that targets open sources may lead to critical consequences because open sources are made available for use by anyone. Meanwhile, GitHub, a widely used open-source management tool, supports the function of code repositories. For that reason, GitHub has been widely used by individuals and renowned businesses for open-source management and distribution. RepoJacking is a type of supply chain attack wherein GitHub's repositories are hijacked and exploited by malicious actors. This study aims to assess the practical feasibility of RepoJacking and verify the vulnerability of a set of repositories publicly released on GitHub against RepoJacking. In addition, methods for protecting against RepoJacking, along with a tool designed to assess the integrity of repositories against RepoJacking, are proposed.
Unravelling the Black Box: Enhancing Virtual Reality Network Security with Interpretable Deep Learning-Based Intrusion Detection System

This study addresses the critical need to secure VR network communication from non-immersive attacks, employing an intrusion detection system (IDS). While deep learning (DL) models offer advanced solutions, their opacity as "black box" models raises concerns. Recognizing this gap, the research underscores the urgency for DL-based explainability, enabling data analysts and cybersecurity experts to grasp model intricacies. Leveraging sensed data from IoT devices, our work trains a DL-based model for attack detection and mitigation in the VR network, Importantly, we extend our contribution by providing comprehensive global and local interpretations of the model’s decisions post-evaluation using SHAP-based explanation.
Empirical Study: Monocular Depth Estimation from RGB, NIR, Thermal Image in Adverse Weather Conditions

Robust spatial understanding is one fundamental condition for safety-aware autonomous driving against adverse weather and lighting conditions, such as rain, fog, haze, snow, and low-light environments. Therefore, numerous autonomous vehicle platforms adopt various sensor modalities to ensure safety and reliability (e.g., RGB camera, NIR camera, thermal camera, LiDAR, and RADAR). Among them, the RGB camera is a commonly adopted complementary sensor because it can provide dense spatial understanding ability compared to LiDAR and RADAR. However, RGB camera is known to be vulnerable to changes in lighting and weather conditions. In this paper, we empirically analyze the robustness of monocular depth estimation from RGB image in diverse seasonal, weather, and lighting conditions. Also, we investigate the robustness of depth estimation from NIR and thermal images in the same condition to find which sensor is robust to environmental changes and capable of dense spatial understanding even in extreme conditions. As a result, we found thermal cameras can provide reliable and robust dense spatial understanding against diverse seasonal, weather, and lighting condition changes.
Multi-View Stereo Using Discontinuity-Aware Planar Parameters

One of the successful approaches in the traditional field of Multi-View Stereo is PatchMatch-based methods. However, because the approaches rely heavily on photometric consistencies, the results are unreliable in low-textured areas. This paper proposes a Multi-View Stereo method that enforces the reliability of similarity costs by utilizing planar parameters refined through spatial consistencies in the PatchMatch process. In the proposed method, initially, depth maps and normals are generated by applying a conventional PatchMatch method. Afterward, a triangular mesh is generated from the selected nodes, and planar parameters are computed from the resulting triangular mesh model. The discontinuity-aware PatchMatch is then processed by leveraging the planar parameters refined through adjacent triangular meshes for similarity cost computation. The results of the experiments demonstrate that the proposed method achieves superior performance compared to existing Multi-View Stereo methods for publicly available High-Resolution images.
An Interactive System for Painterly Image Harmonization

Image harmonization is the process of adjusting the appearance of an object of interest that is copied from a source image and pasted onto a target image to make the composite image look seamless and natural. Painterly image harmonization represents a distinct category within image harmonization where the target image has a painterly style. To obtain the harmonized results, it involves several sub-tasks: segmenting the object of interest from a photograph, creating a naive composition that includes placing and scaling the object onto the target painting, generating a mask for the pasted object, and applying painterly image harmonization to the naive composite image. To facilitate these processes within a unified framework, we integrated object segmentation and painterly image harmonization methods, with a user-friendly graphical user interface. The proposed system enables users to easily create new artworks by placing objects from photos into paintings and harmonizing them according to their intention. In addition, we designed the harmonization pipeline employing a style transfer method and compared it with the conventional harmonization method, E2STN. Qualitative results show the effectiveness of our proposed approach.
Application of Adaptive Polar Code based CV-QKD Scheme for LEO Satellite Systems

The information reconciliation protocol plays a crucial role in achieving a high secret key rate in continuous variable quantum key distribution (CV-QKD) systems. It is closely intertwined with a forward error correction scheme. In systems that operate under dynamic channel conditions, such as low earth orbit (LEO) satellite systems, the utilization of an adaptive reconciliation protocol becomes essential to ensure efficiency. This necessity calls for an efficient rate-adaptive forward error correction scheme, encompassing a single encoder and decoder. This paper provides a comprehensive review of adaptive information reconciliation schemes utilizing polar codes. It also explores their application to LEO satellite systems.
Network Technology under Computer Big Data Network Information Security System Research

This paper proposes a secure data storage scheme for protecting network privacy. In the system hardware design, it is divided into interface module, basic service module and storage module. The three functional modules work together to improve the security of personal privacy data on the Internet. Establish a personal privacy database in software to ensure the security of personal privacy data. Asymmetric cryptography is used to encrypt and decrypt the data. Finally, the encrypted privacy information data is processed centrally to realize the combined storage of privacy information in the computer network. By comparing the safety and operation effect of the system, it is proved that the system has great advantages in safety and efficiency. The simulation results show that the method is effective.
Enhanced Healthcare System with NFT-Prescription

In the domain of healthcare management, conventional paper-based prescription systems manifest vulnerabilities. Addressing this, emerging solutions leverage blockchain and Non-Fungible Tokens (NFTs) to augment e-prescription processes. However, existing research needs comprehensive simulation insights into these NFT-based systems. This paper presents a pragmatic NFT-Integrated E-Prescription Management Smart Contract model to bridge this gap. The model capitalizes on blockchain’s security and NFTs’ attributes to enhance prescription traceability, ownership, and security. It streamlines prescription workflows, facilitating seamless interaction between healthcare providers and pharmacies while also introducing precise ownership control through NFTs. Implemented with a Role-Based Access Control system, authorization is exclusively granted to authorized entities, thereby bolstering security. A comparative analysis reveals distinct disparities in ownership management and prescription expiration. Furthermore, an assessment of cost-effectiveness and robust security measures, encompassing NFT integration, is conducted to safeguard sensitive healthcare data. The model’s applicability is demonstrated through public and local test network deployment. This paper addresses the existing research gap by furnishing comprehensive simulation insights, advancing the comprehension of NFT-based prescription systems.
Experimental Analysis of the Recent Key Recovery Protocol with respect to Commitment Schemes

Recently, Kim et al. proposed a key recovery protocol for (t, n)-threshold ECDSA schemes that enables a user who lost his secret share to recover with the aid of other t users among n users [1]. In their protocol, a commitment scheme was employed to commit to messages, but they provided implementation results of their protocol by employing the Feldman commitment scheme only. In this paper, we examine the efficiency of their protocol with respect to commitment schemes by implementing them. More precisely, we implement the protocol by employing hash-based and Pedersen commitment schemes each as well as the Feldman commitment scheme. Our experimental results show that the hash-based commitment scheme provides the most efficient protocol than other commitment schemes. For example, when t = 3 with 128-bit security, the protocol with the hash-based commitment requires 0.485 ms in total for all computations, while the protocols with Feldman and Pedersen commitment schemes take 7.713 ms and 15.228 ms in total, which improve by factors of about 15.90 and 31.40, respectively.
Structural Anomaly Detection in Advanced Manufacturing Execution Systems

Anomaly detection is crucial to ensuring the robustness and reliability of operational systems. Early identification of anomalies and deviations from standard patterns prevents potential failures, enhances decision-making processes, and optimizes overall system performance. This paper investigates image-based structural anomaly detection for manufacturing execution systems utilizing an optimized VGG16 convolutional neural network to identify structural anomalies. The optimized VGG16 model significantly performs binary classification on the test data to distinguish normal and anomalous instances. A comparative analysis with another classifier demonstrates that the optimized VGG16 is notable with high anomaly detection accuracy and has the potential to improve system reliability significantly. Experimental findings on publicly available image-based anomaly datasets demonstrate the efficiency and effectiveness of the proposed technique in identifying anomalies in management execution systems.
Preliminary study for Conversational Korean-Vietnam Neural Machine Translation

In this paper, we aim to build a conversational Korean-Vietnamese translator. We aim to develop a conversational translator that can be applied to interpreting using conversational data that encompasses features such as ill-formed sentences, anaphora, omissions, and contextual information commonly employed by real individuals in conversations. To this end, we utilized subtitle data to create a large-scale parallel corpus that reflects the characteristics of conversational data and overcome the problem of lack of data between languages, which is a problem in machine translation. We used the built data as training data for a neural network-based automatic translation model to create a conversational translator, which improved the BLEU score by 3.67 compared to the initial experiment.
Deep Learning with Historical Features and Timewise Similarity for Multiple Objects Tracking

Multiple Object Tracking (MOT) in computer vision is a fundamental task focused on identifying and monitoring the movement of multiple objects within a video sequence. MOT plays a crucial role in various applications, including surveillance, autonomous driving, and human-computer interaction. The primary objective is to consistently and accurately follow the trajectories of individual objects across frames while dealing with challenges such as occlusions, and varying appearances. This research paper presents an approach for tackling the challenging task of multiple categories of object tracking using deep learning techniques, combined with the utilization of enriching contextual features during training. In this study, we address the complexities of tracking objects by using temporal-wise similarity to improve features for consecutive frames. To enhance the performance of our tracking framework, we introduce a training strategy by adding to the original dataset a sub-dataset wherein large input images are divided into sub-patches to reach competitive results regarding tracking accuracy and precision with 72.4% and 81.6% on MOT dataset, respectively.