Reviewerly/exHarmony huggingface dataset:
Object Detection in Autonomous Vehicles

Object detection in self-driving cars is a prime component because there is no human control over the cars’ driving. Self-driving cars must detect an object 360 degrees around the car for the car to steer or handle the driving on the correct track. The market for autonomous vehicles is rapidly growing, and as all people now know about the level of technology in the developed countries, which are the United States, the United Kingdom, Germany, England, and Japan, those countries are going all out in the creation of electric and autonomous vehicles. However, most of the current object detection methods are expensive and ineffective. Therefore, in this paper, the new, efficient, and faster design of object detection in autonomous vehicles using the YOLOv5 neural network is investigated, explored, designed, implemented, and evaluated. YOLOv5 is the best model because it is built on the Python framework, is available in a variety of sizes, and is constantly updated and improved. The Python platform, Roboflow tool, and Google Colab were adopted to design the model. Roboflow is great for object detection because of smart polygons, label assist, commenting, and exporting in a variety of formats. Meanwhile, Google Colab is free to use (allows you to run Python code in the cloud), easy to use (user-friendly interface), and offers access to powerful GPUs. The adoption of these with the YOLOv5 neural network is a new attempt in autonomous vehicles. As a result, a better and more efficient object detection method yet affordable for autonomous vehicles was produced and is presented in this paper.
Graph3PO: A Temporal Graph Data Processing Method for Latency QoS Guarantee in Object Cloud Storage System

Object cloud storage systems are deployed with diverse applications that have varying latency service level objectives (SLOs), posting challenges for supporting quality of service with limited storage resources. Existing methods provide prediction-based recommendations for dispatching requests from applications to storage devices, but the prediction accuracy can be affected by complex system topology. To address this issue, Graph3PO is designed to combine storage device queue information with system topological information for forming a temporal graph, which can accurately predict device queue states. Additionally, Graph3PO contains the urgency degree model and cost model for measuring SLO violation risks and penalties of scheduling requests on storage device queues. When the urgency degree of a request exceeds a threshold, Graph3PO determines whether to schedule it in the queue or initiate a hedge request to another storage device. Experimental results show that Graph3PO outperforms its competitors, with SLO violation rates 2.8 to 201.1 times lower.
VSP-Based Warping for Stitching Many UAV Images

Image stitching aims to mosaic individual images together to build a broader panorama, wherein image registration is the most critical step. In this step, all subimages are normally brought into alignment by warping functions, each of which is often parameterized by rotation, scale, and translation factors. Existing methods usually have to estimate such parameters simultaneously, which incur the problems of unstable and time-consuming computations, especially for stitching large number of unmanned aerial vehicle (UAV) images. In this article, a novel stitching method using vector shape preserving (VSP)-based warping is proposed, which is especially suitable for stitching many UAV images. We innovatively construct warping vectors and involve them to measure registration error to preserve vector shapes, which allows separately dealing with a re-plane stage and a translation stage. Additionally, a novel scale regularization processing connected to the warping is designed for tractable computation. Our method is able to achieve low computational cost, high alignment accuracy, and meanwhile valid real-world interpretation. Experimental results on four real-world UAV image datasets validate the excellent performance of our method.
Research on the air traffic control radar power diagram based on measured data

The radar power diagram is an important indicator proposed for radar, which directly affects combat performance. This article introduces the overall design process and development tools for drawing power diagram based on measured data, and briefly describes the drawing method of power diagram. Using MATLAB and Excel tools, combined with the measured data reported by a certain type of air traffic control radar, the measured radar diagram is plotted using this method, making a beneficial attempt for the application of radar power diagram in practical performance testing.
Research on High-integrated Low-latency Superconducting Quantum Readout System

Superconducting quantum computing has gained significant attention for its ability to prepare a large number of qubits easily. However, the superconducting quantum readout systems, which serve as the foundation of superconducting quantum computing, still face several challenges including low integration, high computing delay, and reduced flexibility. In this paper, a highly integrated and low-latency superconducting readout system is introduced. This system, built on RFSoC chip, comprises multiple analog-to-digital and digital-to-analog conversion channels, as well as demodulation calculation units, to improve the degree of integration. The system employs a time-optimized single-channel matching filter demodulation algorithm to address the problem of high delay associated with readout systems, achieving a demodulation calculation delay of 19.2ns. Additionally, the system supports multiple quantum measurement and control experiments through the parameterization and modular design of functional modules. The actual test reveals that the qubit fidelity F00 and F11 obtained with this system can reach 99.5% and 98.6%, respectively, while the readout speed is significantly enhanced compared to software-based calculation. This system effectively improves the accuracy of quantum state readout.
Accelerating AI performance with the incorporation of TVM and MediaTek NeuroPilot

The continuing prominence of machine learning has led to an increased focus on enhancing the inference performance of edge devices to reduce latency and improve efficiency. Two widely adopted strategies for accelerating computational performance are quantisation and the utilisation of AI hardware accelerators. Each type of accelerator or inference engine offers distinct advantages, with accelerators primarily designed to optimise neural network operations. In this paper, we present an innovative method for integrating TVM's quantisation flow with the MediaTek Neuropilot AI accelerator. We outline the process of converting the TVM relay intermediate-representation quantised neural network dialect model to a tensor-oriented quantisation format, with the aim of harnessing the full potential of both TVM and MediaTek NeuroPilot. This integration enables more efficient neural network inference while preserving the accuracy of the results. We assessed the effectiveness of our proposed integration by conducting a series of experiments and comparing the performance of our approach with that of TVM equipped with an autotuning mechanism. The findings indicate that our approach substantially outperforms TVM in both floating-point model inference and quantised model inference, with inference speedups of up to 11× and up to 70×, respectively. These results underscore the potential of our approach in accelerating AI performance across a diverse range of applications and edge devices. Moreover, a key contribution of our work is providing a valuable practical method for other hardware companies interested in integrating TVM with their own accelerators to achieve performance gains.
A Framework for Leveraging Interimage Information in Stereo Images for Enhanced Semantic Segmentation in Autonomous Driving

Semantic segmentation is a crucial task with wide-ranging applications, including autonomous driving and robot navigation. However, prevailing state-of-the-art methods primarily focus on monocular images, neglecting the untapped potential of stereo cameras commonly equipped in autonomous vehicles and robots, which capture binocular images. In this article, we aim to introduce an innovative stereo-vision-based semantic segmentation framework that maximizes the utilization of stereo image data to enhance segmentation performance. Unlike conventional monocular approaches that only use one image, our method effectively uses both the images, exploiting interimage correspondences and harnessing previously neglected information. Our core innovations encompass label generation for right images, combined with stereo-vision-based information fusion. For label generation, we propose a novel technique to accurately generate labels for the right images in stereo pairs, even in scenarios with no direct annotations. This innovative approach empowers our models to effectively learn from a complete stereo dataset, enhancing their semantic segmentation capabilities. In addition, our stereo-vision-based information fusion framework seamlessly integrates features and spatial disparities from the binocular images, enabling our models to produce more accurate and contextually enriched semantic segmentation outputs. To validate the efficacy of our proposed approach, we conduct comprehensive experiments on the Cityscapes and KITTI datasets using diverse, well-known semantic segmentation architectures. The results unequivocally demonstrate the superiority and effectiveness of our method.
Research and Implementation of Recommendation System Based on Neural Network

The recommendation system mines the user's interest preferences based on the user's interaction history on the network to complete the user's personalized recommendation. Collaborative filtering, as a typical algorithm in personalized recommendation, is also often used in recommendation systems. But collaborative filtering still has sparse and cold start problems. In this regard, researchers usually provide recommended performance by obtaining auxiliary information such as the project's own attributes. In order to solve these problems, this paper expands the user's potential interest by using the knowledge graph to combine the user's historical interaction information. Taking the user's interaction history as a starting point, iteratively form multiple entity sets in the knowledge graph, and finally form the user's potential interest distribution by superposition, which is used to predict the click probability. Through experiments on different data sets (movies, books), the algorithm confirmed its effectiveness on these issues.
A Transformer-Based Language Model for Sentiment Classification and Cross-Linguistic Generalization: Empowering Low-Resource African Languages

Sentiment analysis is a widely studied area of natural language processing (NLP), however, a lot of attention has been given to languages with higher resources, like English. By concentrating on low-resource African languages including Hausa, Nigerian Pidgin, Igbo, Yoruba, Swahili, and Kinyarwanda for which there is a lack of curated datasets for sophisticated AI applications. This research intends to close the gap by examining the viability of current NLP strategies and creating new ones to meet this challenge. Sentiment analysis enables us to comprehend the feelings and viewpoints expressed in textual information. A transformer-based language model is presented in this research for low-resource African languages in order to classify sentiment and generalize across languages. We fine-tune the XLM-RoBERTa model on Hausa, Nigerian Pidgin, Igbo, and Yoruba, and then assess its performance in these languages as well as Swahili and Kinyarwanda, which the model had not previously seen. Our findings show that, even in languages with few linguistic resources, the model is effective at performing sentiment analysis tasks for known and unknown languages. This research helps close the digital language gap by extending natural language processing capabilities to under-resourced African languages.
Research of the Cyber Attack and Defense Behavior Modeling Method Based on Object-oriented

In order to describe the cyber attack and defense countermeasure behaviors effectively, an object-oriented modeling method is proposed, a hierarchical and classified cyber attack and defense behavior modeling framework is designed. The modeling process is divided into three stages: object-oriented behavior requirement analysis, behavior model design and the model implementation. In the model design process, the model is abstractly described in three aspects: conceptual modeling, logical modeling and procedural modeling. Finally, based on the object-oriented modeling method, constructed a DDOS attack behavior model simply, and it is initially verified that the method can be effectively used for the modeling of cyber attack and defense behavior.
Research on random forest algorithm based on oversampling and feature selection

Compared with other classification algorithms, random forest algorithm has obvious advantages in terms of classification accuracy, generalization error and training speed, and is widely used in data mining. However, when faced with unbalanced data, the classification performance of random forest algorithm can be greatly limited. To better deal with imbalanced data, an improved random forest algorithm (oversampling &FeatureSelecctionRandomForest,OF_RF) based on oversampling and feature selection is proposed in the paper. The algorithm firstly, from the data level, uses the Borderline-SMOTE algorithm to preprocess the imbalanced data set and introduces the LOF algorithm to improve the Borderline-SMOTE algorithm in order to eliminate outliers and thus improve the processing performance for negative class samples. Secondly, from the algorithm level, the ReliefF algorithm is used to assign different weights to the balanced processed data, while excluding irrelevant and redundant features, so as to perform dimensional simplification. Finally, the classification performance of the algorithm is further improved by using the weighted voting principle. The experimental results show that the improved OF_RF algorithm exhibits higher evaluation metrics in processing unbalanced data compared with the traditional random forest algorithm, proving that the algorithm has significant advantages in classification performance of unbalanced data.
A Meteor Detection Algorithm for GWAC System

Compared with the international meteor surveillance systems, the ground wide angle camera (GWAC) system exhibits characteristics such as images with the resolution of 4K × 4K and single-site observation. These characteristics present challenges for meteor detection in the GWAC system. Consequently, this paper proposes a new meteor detection algorithm for the GWAC system on the base of the solely mini-GWAC system data algorithm. The new algorithm consists of the following key steps: (1) to compare differences between adjacent frames, applying block-based image binarization thresholds, and incorporating median filtering to reduce noise; (2) to adopt the probabilistic Hough transform (PHT) to identify moving objects and cluster them based on the origin moment of the line segments, while assessing the credibility of clustering; (3) to introduce the so-called maximum disappearance frame for moving objects in the tracking algorithm, enhancing the ability to track multi-frame moving objects. The utilization of the line segment inclination angle of the moving object as the direction of movement facilitates the tracking of multiple moving objects, thereby reducing the probability of mistakenly selecting single-frame moving objects; (4) to leverage the light curves of single-frame moving objects to select meteors to enhance the accuracy of meteor detection. Comparative experiments demonstrate that our proposed algorithm processes each frame image in just 0.39 s, achieving an accuracy of 89.8% in the dataset of 5856 adjacent frames. The experimental results indicate that the algorithm achieved an accuracy of 90.27% when applied in the meteor detection of the image data captured by the GWAC system from Dec. 10th to 19th in 2019 and 2021, obtaining excellent detection results.
Immersive Learning Research from the Perspective of Its Researchers and Practitioners: Questionnaire Validation and Early Results from a Survey on a Conceptual Framework for the Field

Immersive learning research is a field of study that emphasizes diversity of scholarship and subject areas. This diversity presents a challenge for understanding the breadth and depth of the field of immersive learning, a challenge that led to the Immersive Learning Research Network’s call for the community of immersive learning researchers to develop a conceptual framework supporting a common understanding of this diverse field - The Immersive Learning Knowledge Tree. However, this structure has not had its underlying assumptions validated by the larger, diverse community of immersive learning researchers and practitioners. Thus, we developed, validated, and disseminated across associations of the field a questionnaire for analyzing the assumptions, structure, and relevance of the Knowledge Tree proposal. Early results point towards overwhelming agreement from the community on the premise that the field of immersive learning research is muddled/fragmented, the current knowledge partially disjointed, specifically among different disciplines (Q3), due to its interdisciplinary nature. There are also strong indications supporting the premise that researchers active in the field of immersive learning research desire to combine their efforts with others.
Diffusion model for mural image inpainting

This paper focuses on crack detection and digital inpainting of ancient fresco cultural heritage, and proposes a method to use diffusion model for fresco inpainting. It effectively preserves the detail information of the frescoes and reduces the damage to the original frescoes in the inpainting process. Finally, a diffusion model is used to digitally repair the mural, and the repair performance is optimized by adjusting the parameters to achieve natural repair of mural cracks. The experimental results show that the model in this paper has significant advantages in the digital restoration task and provides a new effective method for the conservation and restoration of ancient frescoes. In addition, this study has important implications for the technological development in the field of mural restoration and heritage conservation, and contributes to the progress and technological innovation in related fields.
Secure and Privacy-Preserving Aggregation Scheme for Traffic Management Systems

Traffic delays and congestion will likely worsen as cars on the road rise. Autonomous vehicles (AVs) can help solve these problems by reporting their routes’ information to control traffic congestion. However, current route reporting schemes ask AV users to disclose their route’s information, which breaches the user’s privacy. In this paper, we offer an aggregation over encrypted data technique for traffic management systems to solve the difficulty of privacy-preserving route reporting for AV users. The area of interest is divided into several groups/communities. Every community will be controlled by a road-side-unit (RSU) to collect and aggregate the route information for the AVs inside its group/community. In addition, we used a group signature scheme to ensure the membership of the AV within the group/community without disclosing its real identity. Our security and privacy analysis show that our proposed scheme can achieve the required design goals. Also, our performance evaluation shows that our scheme achieves low communication and computation overheads.
Research on Visualization Method for Detection Power of Guidance and Warning Radar

The radar power map is a representation of radar detection performance. Due to the difficulty in measuring the power indicators of the guidance and warning radar, many radar units lack objective and accurate basis for evaluating the power of the radar during the use of radar equipment. The traditional radar power map drawing mainly relies on target aircraft flight inspection (inspection) data and mathematical model calculations. This article studies the method of correcting the free space power map based on a large amount of flight data accumulated after the deployment of radar positions. A method of correcting the detection distance and elevation of guidance and warning radar based on measured data is proposed, and the calculation method of radar detection probability based on measured data is discussed. A study was conducted on the methods for digitizing power maps. It has accurately reflected the radar detection performance, providing an effective reference for the radar to exert more combat effectiveness. Finally, the effectiveness of this correction method was verified through comparison.
QoS-Aware Federated Crosschain-Based Model-Driven Reference Architecture for IIoT Sensor Networks in Distributed Manufacturing

The realm of the Industrial Internet of Things (IIoT) encompasses a broad spectrum of sensors that are integral to distributed smart manufacturing (DSM). The miscellaneous IIoT sensors deployed for DSM are distributed and operate in a hierarchical and federated structure. Nonetheless, fulfilling essential quality of service (QoS) requirements, such as ensuring security and privacy (integrity) while maintaining scalability and interoperability (robustness), poses a profound challenge for the DSM cloud service platform. Although blockchain technologies have been used to safeguard integrity, the first two generations have imposed constraints on robustness. In contrast, the third-generation blockchain, a.k.a. decentralized crosschain ecosystem, complements Web 3.0 and metaverse and can mitigate the constraints of previous generations. Moreover, blockchain-based ad hoc solutions for DSM use cases are abundant; they often suffer from limited adaptability and unique or homogeneous use cases from a software engineering perspective. A holistic architectural modeling process (AMP) leading to a software reference architecture (SRA) is preferred when alleviating ad hoc constraints. Therefore, we proposed an AMP for SRA for crosschain-based DSM that safeguards integrity while preserving robustness. In the work described in this article, we have conducted the following system—modeling process. First, we propose a novel software AMP for DSM. Second, we deduce a modeled SRA based on a crosschain. Third, we infer a modeled system architecture (SA). Empirical experiment results demonstrate that our proposed crosschain-based method outperforms the widely used on-chain-based method while achieving our objectives efficiently.
Analysis and Research on Internet Teaching in Colleges and Universities Based on Apriori Algorithm

Internet teaching brings convenience to college students’ autonomous learning, and it brings brand-new innovation to higher education mode. Internet teaching can break through the limitation of teaching time and region. Taking the online course of big data application technology as an example, this paper uses Apriori correlation algorithm to mine students’ online learning records and learning behaviors, designs course content support, and uses correlation matrix after cluster analysis to transform Apriori algorithm, which has great reference value for the guidance of teachers’ teaching time and teaching methods and the cultivation of students’ comprehensive quality.