Reviewerly/exHarmony huggingface dataset:
Research on distortion correction of fisheye image based on improved linear feature model

The fisheye lens is a kind of ultra-wide angle camera, which has the characteristics of short focal length and large angle of view. It is widely used in video conference, security monitoring, intelligent transportation and other fields. However, the large viewing angle advantage of the fisheye lens is at the cost of very serious distortion. In order to obtain effective scene information in the picture, it is necessary to correct the distorted image to a distortion free linear perspective projection image. This paper studies the characteristics of nonlinear imaging models and proposes a distortion correction algorithm of fisheye image based on improved linear feature model. The algorithm uses Taylor polynomial to approximate the general model of fisheye lens, proposes the method of backward coordinate mapping for distortion correction. The experimental results show that the method proposed in this paper has better performance in terms of qualitative characterization of pixel error and re-projection error.
Digital Human Interaction Based on Mono Camera for Digital Twin

In virtual reality, the importance lies not only in digitally replicating real spaces, assets, forms, and actions but also in digitalizing the movements of individuals within these spaces from the perspective of digital twin technology. However, traditional methods, which require the use of Head Mounted Displays (HMD), VR devices, and various sensors attached to humans, impose significant constraints when reflecting the diverse actions of users within the actual digital twin. To overcome this, we introduce the "Digital Human Interaction based on Mono Camera" method. This approach simulates full-body human actions using an easily accessible mono camera. Through this method, we’ve identified the potential to broaden the range of user movements that can be mirrored in virtual reality while significantly reducing costs. Crucially, our approach is future-oriented and universally applicable because it does not require specialized equipment or sensors.
Quantum Federated Learning for Vehicular Computing Scenarios

With the rise of autonomous vehicles (AVs), the number of self-driving vehicles has increased in recent years and vehicular computing (VC) was actively used to ensure road safety by controlling AVs. However, the recent escalation in demand for a massive scale vehicle network has prevented VC from achieving its purpose. To overcome this problem, a framework which combined VC and federated learning (FL) was proposed. Although the vehicular FL was initially effective, it also faced challenges as the excessive increase in the amount and size of data generated by AVs and the serious threat of data leakage have severely degraded the performance of vehicular FL. Thus, quantum computing was exploited to further improve on the pre-existing FL and proposed dynamic quantum federated learning (DQFL). This work proposes the application of DQFL to public safety scenarios to investigate the effectiveness of the model in controlling the AVs under real-life constraints.
Few images based 3D Reconstruction of Korean Wooden Architecture Bracketing System(Gongfo)

Three-dimensional (3D) reconstruction from 2D input images is a fundamental task in computer vision and has numerous applications in fields such as robotics, augmented reality, and cultural heritage preservation. This paper presents a technique for performing 3D reconstruction of Korean Wooden Architecture Bracketing using Structure-from-Motion(SfM) and Multi-View_Stereo(MVS) technology. SfM leverages the geometric relationships between multiple images to estimate camera poses and reconstruct the underlying 3D scene. the MVS algorithm is used to complete the dense reconstruction of objects. We show that few iages based 3D reconstrution results for korean wooden architecture bracketing system named by Gongfo in korean.
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection

Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for efficient storage management methods while preserving high-fidelity audio. A hardware-perspective approach, such as using a low-resolution microphone, is an intuitive way to reduce file size but is not recommended because it fundamentally cuts off high-frequency components. On the other hand, a computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended because it also provides a corresponding decoding method. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method. The pre-trained autoencoder is trained for the purpose of audio super-resolution so it can be utilized to encode or decode any arbitrary sampling rate. Moreover, it will reduce the communication cost for data transmission from the edge to the central server. Via the comparative experiments, we confirm that the zero-shot audio compression and decompression highly preserve anomaly detection performance while enhancing storage and transmission efficiency.
Towards On-Board Image Compression using Vector-Quantized Auto Encoders

In the CORSA project [1] we demonstrated an AI method for lossy image compression for Sentinel-2 data using the concept of vector quantized auto-encoders. We now extend this method to hyperspectral images and investigate the requirements to run the algorithm on-board. Trade-offs include the model size, number of bands that are compressed simultaneously, the use of separable convolutions and model quantization techniques.
Lattice surgery-based logical operations in a fault-tolerant quantum software framework

We present a quantum software framework that supports fault-tolerant quantum computing. This framework uses the lattice surgery technique to encode logical qubits in surface codes and implements logical Clifford and T gates. By interfacing the QPlayer simulator with the framework, we have configured six two-dimensional logical qubits with a distance of three and have evaluated the lattice surgery-based logical operations, which have been presented theoretically by simulating quantum circuits composed of universal quantum gates. In conclusion, we have shown that the proposed framework can effectively perform Clifford and T gates in fault-tolerant quantum computing based on surface code logical qubits.
An Improvement of A Lightweight NFC Authentication Algorithm Based on Modified Hash Function

In NFC applications, user privacy information must be protected first. Cao and Liu recently proposed a lightweight NFC authentication scheme based on an improved hash function to ensure that the user’s private information will not be leaked. Although their method is highly efficient and has Mutual Authentication, Forward Security, Backward Security, and security against attacks such as Replay, Location, and Fake, once out of synchronization occurs, the method must reestablish synchronization data. Therefore, this article will propose a lightweight synchronization method.
Blockchain-based Federated Learning for Bearing Fault Detection in the Industrial Internet of Things

The reliability of Industrial Internet of Things (IIoT) systems requires robust fault detection, which can be achieved with AI. However, the current centralized learning approach is inefficient. Federated Learning (FL) solves this problem by enabling distributed training without exposing individual information. This article proposes a parameter aggregation technique for bearing fault detection in IIoT using a lightweight smart contract with a PoA-based blockchain consensus. The findings indicate that the proposed system provides a secure aggregation process with an accuracy of 94.00% and a processing time of 1.54 s, which is suitable for the IIoT environment.
Optimizing UAV Navigation through Non-Uniform B-Spline Trajectory for Tracking UAV Enemy

This paper proposes optimizing unmanned aerial vehicle (UAV) navigation through a non-uniform b-spline trajectory. The safety of the UAV and successful trajectory execution while avoiding obstacles become paramount in scenarios involving enemy tracking. The proposed system offers optimization in UAV navigation by adjusting time and velocity to address smooth and safe path planning in a dynamic environment for tracking UAV enemies. The tracking enemy relies on a depth camera to classify the enemy with the environment and calculate the distance. Specifically, the UAV capitalizes on the flexibility and smoothness of Non-Uniform B-Spline curves, enabling UAVs to navigate complex environments with precision. The approach is evaluated through comprehensive the experimental result.
Incorporating text and emoticon features in sarcasm and irony detection: A deep learning-based approach

Emotions play a crucial role in social media interactions, and sarcasm and irony often convey complex emotions that can be challenging to detect accurately. This study focuses on automating the recognition of sarcasm and irony in Chinese through various computational methods using machine learning algorithms. This study utilizes both text and emoticon/emoji feature to improve detection accuracy. The study employs a combination of linguistic rules, word embeddings, and neural network models for detecting irony in Chinese microblog comments. The results highlight the advantages of deep learning techniques and pre-trained word embeddings in enhancing the accuracy of irony detection. Furthermore, the study demonstrates the viability of the proposed models by focusing on microblog posts related to social events. These contextualized experiments show promising results and pave the way for future studies to explore other domains and data sources. Overall, this study has made significant contributions to the field of irony recognition research and laid the groundwork for more complex models in the future, with practical implications for enhancing sentiment analysis in various fields.
Kidney Tumor Segmentation and Grade Identification in CT Images

Kidney tumor grade identification by means of feature based classification combined with semantic segmentation of Computed Tomography (CT) images targeting tumor region extraction are the two main contributions of this paper. For all the patients involved in the study three phases of the CT examinations are considered: native, arterial and venous phase. Medical specialists have annotated the images marking the regions in which tumors reside for each of the three phases. On each type of phase deep learning based segmentation has been applied in order to identify the tumor area. The differentiation among various tumor grades by means of deep learning is extremely challenging due to the small amount of available image data and due to the high similarity between different tumor grades. Hence feature based classification is applied to segmented regions in order to distinguish among the four tumor grades. The accuracy of the recognition varies from 60% up to 90% depending on the tumor grade, with first grade tumors being more difficult to be recognized while fourth grade tumors are correctly identified for most patients.
Implementation of deep learning based intelligent image analysis on an edge AI platform using heterogeneous AI accelerators

Recent advancements in artificial intelligence (AI) technology have spurred efforts to implement deep learning-based intelligent image analysis in various video surveillance applications. However, traditional approaches to using remote cloud computing platforms results in increased network latency and potential breaches of personal information. To address these issues, the adoption of edge AI, utilizing deep learning-based data analysis directly on the devices where image data is collected, has emerged as a promising solution. In this paper, we introduce a practical edge AI platform that enables real-time deep learning-based image analysis, including object detection and multi-person pose estimation. The platform is built on an embedded board equipped with heterogeneous AI accelerators, facilitating parallel inference of multiple deep learning-based image analysis models. In addition, the platform applies task-level pipeline parallelism to maximize the utilization of computing resources, which leads to a reduction in overall image analysis latency. Experimental results demonstrate the effectiveness of our edge AI platform in providing real-time intelligent video analysis services.
Im2Vide0: A Zero-Shot approach using diffusion models for natural language conditioned Image-to-Video

Recent breakthroughs in the study of Denoising Diffusion Models have constituted a driving factor in advancing the field of Computer Vision through the aid of generative Machine Learning. Challenging problems, such as image or video synthesis, become intertwined with Natural Language Processing techniques to push the boundaries of computational capabilities and foster human ingenuity. We present an approach to solving text-guided video completion, a difficult task of producing the temporal future progression of an image, conditioned by textual description. Our proposed framework applies the latest academic leaps in Text-to-Image and Text-to-Video generation to perform Image Animation guided by text, an insufficiently explored topic due to its strenuous nature. We present how adapting generalized pre-trained Text-to-Image models can achieve good performance solving the described problem in a Zero-Shot context. Our experimental evaluation shows that the proposed solution is robust, capable of animating any source image guided by a language prompt, by inheriting the generality of the backbone pre-trained Text-to-Image model.
Design and Implementation of Data-Intensive Application using Memory Expansion Device

Modern intelligent applications utilizing data-driven analysis have gained significant attention. As these applications rely on analyzing large-scale data for improved performance, they demand substantial high-performance memory. However, current computer architectures often lack the capacity to provide enough fast memory, leading to performance degradation in such applications. To address these limitations, memory expansion devices are being developed that connect to host servers through the new cache coherent interconnects like CXL. These devices offer additional memory and hardware acceleration capabilities to enhance performance.In this paper, we propose a novel method to accelerate data-intensive MPI applications in a CPU-FPGA heterogeneous computing environment, employing memory expansion devices called MEX. Our approach leverages MEX and MPI to enable near-memory processing by utilizing MEX as shared computing resources. To the best of our knowledge, this is the first study to combine memory expansion devices and MPI for accelerating data-intensive MPI applications. We implement a content-based image similarity search system using MPI and MEX and verify the feasibility of our proposed methods.
Recognizing Sentiments In Regional Language Text From Social Media Using Machine Learning Approach: A comparative Study to understand the state of the art tools and associated functionalities available

Regional language contents gain the highest traction in today’s social media network marketing and from users’ perspectives since nearly 90% of users consume contents in regional language. Those who are only focusing on English as the major language of social media or website contents are clearly missing the majority. Social media currently is touching the untouched digital population of the world through creative contents, easy to use convincing features and infinite possibilities of enhancing personal social networks using regional language like Hindi, Marathi, and Gujarati and many other state and regional languages. This has created immense opportunities in the area of text mining, text analysis and text generation too many scholars and researchers to visualize and analyses the contents to gain insights into what are the multidimensional perspectives of the authors of any such contents. This research work is going to understand the current work done in the area of regional language sentiment analysis in India and all across the globe to understand the status and research gap in the domain to work further in the Maharashtra state regional language contents available on social media pages. This research work will try to explore all possible tools and technologies available and used by the recent research scholars and industry leaders to decide the way ahead to work in the Marathi regional language domain in near future.
Decentralized one stop solution for real estate

Real estate investing has traditionally been a complicated and opaque process, but blockchain technology and tokenization can provide a solution. Investors can gain access to a previously inaccessible asset class by using blockchain-based tokens to represent fractional ownership of real estate. This paper explores the feasibility of a tokenization-based real estate investment project and the pros and cons of using blockchain-based tokens for fractional ownership. The platform, called “Estate-O-Blockchain,” will allow users to invest in real estate with digital tokens, creating a new and more accessible form of real estate investment. The platform will provide an easy-to-use interface for buying and selling digital tokens, as well as a secure and transparent platform for tracking investments and real estate ownership. In addition, Estate-O-Blockchain will provide users with access to detailed real estate information, including property history, financial data, and property management information. This project has the potential to revolutionize the real estate investment industry by providing a secure and accessible platform for real estate investment.
A Differential Privacy-Based Approach for Protecting Crowdsourced WiFi RSS Data

The proliferation of WiFi access points has significantly transformed our interactions with the digital world. However, the crowdsourced collection and dissemination of WiFi access point data have raised considerable concerns regarding privacy. The availability of such data can facilitate the identification of access points, and the presence of identifiable information, including names, can enable inferences regarding the location of an individual’s residence, thereby jeopardizing the privacy of sensitive personal information. In response to these concerns, we propose a novel approach to privacy-preserving crowdsourced WiFi data publishing, utilizing differential privacy. This approach ensures that the released data maintains the confidentiality of WiFi access point owners while enabling useful analysis of the network data. The objective of this article is to investigate the challenges and possibilities associated with the use of differential privacy in crowdsourced RSS data publishing for WiFi access points, with an emphasis on preserving privacy. The proposed method is evaluated in a simulated environment using Mininet-WiFi.
Federated Learning for Intent Classification

This research focuses on Federated Learning for intent classification as a core element to any conversational system. Since the level of accuracy needed for intent classification is typically quite high and considering the challenges with accessing enough conversational data with which to train and develop a classification models, we exploit Federated Learning to evaluate its feasibility in improving classification accuracy over more traditional, centralized approaches. The research findings are positive, showing an increase in performance across all metrics and achieving a higher accuracy on average across all the tests conducted.
Explainable Adversarial Mitigation Framework for Zero-Trust Cyber Warfare

The Zero-trust security architecture is a paradigm shift toward resilient cyber warfare. Although Intrusion Detection Systems (IDS) have been widely adopted within military operations to detect malicious traffic and ensure instant remediation against attacks, this paper proposed an explainable adversarial mitigation approach specifically designed for zero-trust cyber warfare scenarios. It aims to provide a transparent and robust defense mechanism against adversarial attacks, enabling effective protection and accountability for increased resilience against attacks. The simulation results show the balance of security and trust within the proposed parameter protection model achieving a high F1-score of 94%, a least test loss of 0.264, and an adequate detection time of 0.34s during the prediction of attack types.
Collaborative Multimodal Diagnostic: Fusion of Pathological Labels and Vision-Language Model

Large vision-language models are remarkable and significantly improve the semantic understanding of images, and demonstrate the ability in varies tasks. For those models, they were mostly trained using massive datasets with image-text pairs across various scenarios. Nevertheless, most of them provide a general understanding and superficial semantic information about images, and cannot infer in-depth results in specific domains, such as radiology. Model fine-tune technology with biomedical reports can be applied to enhance the performance of analytics, however, it is resource-intensive and difficult to further expand. In this work, we proposed a novel framework combining computer vision network and vision-language model, which is able to analyze and answer the query on the chest radiographs. A state-of-the-art network is used to pre-process the radiographs and extract the detailed pathological labels. The relevant label information will be integrated into the flow as a system message prompt, allowing the vision-language model to deduce deeper levels of disease information without the need for prerequisite model fine-tuning. Our approach opens up a new methodology for chest radiograph analytics and reasoning about biomedical images.
Comparative Analysis of Different BERT-Based Machine Learning Models for Hostile Hindi Content Detection

Platforms on social media increasingly offer hostile content. As a result, exact hostile post detection is now necessary in order to launch the proper response. Language comprehension now faces new challenges due to the increasingly hostile content over different electronic medias. Language barriers increase the difficulty. Even though there have been many research conducted in English, regional languages haven’t made much progress as the necessary tools and datasets aren’t currently accessible. In this research, contextual embedding method based on Bidirectional Encoder Representations from Transformers (BERT) is combined with Support Vector Machine (SVM) to distinguish Hindi Hostile or Non-Hostile posts on social networking sites by employing Constraint 2021 Hindi Dataset. Various cutting-edge BERT-based methods are also compared and analyzed with machine learning methods like Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP) in this research work. For the four hostile subclasses (Defamation, Fake, Hate, and Offensive), it is discovered that the performance of the model we propose (Indic-BERT+SVM) surpasses the baseline model with F1-Score of 96.49 for binary (hostile/non-hostile) classification task and F1-Scores of 44.63, 76.00, 56.93, 61.80 respectively for multi-class, multi-label (defamation, fake, hate, and offensive) classification tasks.