Reviewerly/exHarmony huggingface dataset:
Research on Path Planning of Intelligent Vehicle Expressway

Path planning is one of the technologies of intelligent vehicles, and planning a reasonable path is beneficial for the safety of automobile driving, to address the problem of poorly oriented search objectives in RRT* algorithm planning, two methods are used during the sampling process: target point biased sampling and sampling point area limitation, Further constraining the planning path and sampling region allows the tree to grow only towards the region of the target location, reducing the randomness of the planning process, Collision detection is implemented by using the obstacle vehicle volume inflation method to keep the path away from obstacles. Furthermore, a Bezier curve is utilized to fit and optimize the path. The improved algorithm is simulated and validated for lane changing scenarios of intelligent vehicles on highways. The results demonstrate that the algorithm successfully plans the path for the intelligent vehicle to perform lane changes.
An Automated Parking Trajectory Planning Method Based on Safe Parking Corridors

For the auto-parking trajectory planning problem, one method is to transform it into an optimal control problem (OCP) and tackle it. However, it is challenging for planning a safe, comfortable trajectory in the open space with various irregular obstacles or narrow scenes due to non-convex obstacle avoidance constraints on a large scale and complete obstacle avoidance in narrow scenes. Inspired by this, a trajectory planning algorithm based on safe parking corridors is proposed. Firstly, an initial trajectory is determined by applying Hybrid A* algorithm, while a precise collision detection module is also introduced to enhance the reliability of Hybrid A* specifically in narrow scenarios. Such obtained trajectory is used as warm start to accelerate the solution of OCP. Additionally, the safe parking corridor constraint is adopted to represent the obstacle avoidance constraint. In this context, not only the computational scale will be reduced, but also the environmental complexity will be avoided. Simulation results demonstrate that the proposed method exhibits robustness with significant improvement in the success rate of planning in narrow and intricate settings.
Deep Hybrid Architecture for Suspicious Action Detection in Video Surveillance

For Ensuring safety for the public and their property, detection of suspicious actions from video surveillance is an important application in the rapid development of computer vision technology. In this paper, we propose a Deep Hybrid architecture for detecting suspicious activities in surveillance video. In this case, there are four stages in identifying abnormal actions: preprocessing, segmentation, feature extraction and detection. Initially, the video undergoes the preprocessing stage, here the video is converted into frames the Gaussian filtering method is applied to eliminate noise. Subsequently, frames are segmented using a deep joint segmentation model. This process takes place in the segmentation stage. Then features like improved LGXP and SLBT features are extracted in the feature extraction stage. Modified Gabor filter is employed for the extraction process of the improved LGXP feature. The hybrid classification model is used for identifying suspicious actions. The combined SLBT and improved LGXP features are given as input to the hybrid classification model. This model is a combination of CNN and Bi-GRU frameworks. The detection of abnormal actions is by averaging the outcome of CNN and Bi-GRU. The model detects the suspicious activities like traffic collisions, attacks, mistreatment and gun firing.
Research on Off-road Environment Perception Based on Multi-sensor Fusion

In response to the challenges of complex and changing terrain, lack of obvious road boundaries, and serious dust situations during vehicle operation in off-road environments, this paper proposes an off-road environment perception architecture that includes ground point segmentation and object detection. Firstly, a non-data-driven approach is studied for ground point segmentation in off-road winding paths to ensure real-time performance and interpretability of the algorithm. Secondly, data features are analyzed to develop a noise point filtering scheme based on point cloud intensity and density features. This helps to remove unwanted noise points in the data. Additionally, considering the characteristics of semi-solid-state LIDAR, an improved Euclidean clustering method is researched for obstacle detection. Finally, the clustering results are fused with the 2D object detection results to obtain the final perception output. The method is validated on data collected in off-road environments, and the results demonstrate that it can effectively distinguish ground points from non-ground points, remove over 97% of dust points, and accurately identify objects.
A Comprehensive Study on Model Initialization Techniques Ensuring Efficient Federated Learning

Advancement in the field of machine learning is unavoidable, but something of major concern is preserving the privacy of the users whose data is being used for training these machine learning algorithms. Federated learning(FL) has emerged as a promising paradigm for training machine learning models in a distributed and privacy-preserving manner which enables one to collaborate and train a global model without sharing local data. But starting this learning process on each device in the right way, called “model initialization” is critical. The choice of initialization methods used for models plays a crucial role in the performance, convergence speed, communication efficiency, privacy guarantees of federated learning systems, etc. In this survey, we dive deeper into a comprehensive study of various ways of model initialization techniques in FL.Unlike other studies, our research meticulously compares, categorizes, and delineates the merits and demerits of each technique, examining their applicability across diverse FL scenarios. We highlight how factors like client variability, data non-IIDness, model caliber, security considerations, and network restrictions influence FL model outcomes and propose how strategic initialization can address and potentially rectify many such challenges. The motivation behind this survey is to highlight that the right start can help overcome challenges like varying data quality, security issues, and network problems. Our insights provide a foundational base for experts looking to fully utilize FL, also while understanding the complexities of model initialization.
Evaluating LSTM and GRU Models for Cryptocurrency Price Forecasting in Financial Markets

Cryptocurrencies are a type of digital money distinguished by a decentralized system that uses encryption to authenticate transactions and keep records, obviating the need for a central authority. A key element of these digital assets is the decentralization of power from a single entity to a dispersed network. The extreme price volatility of cryptocurrencies, on the other hand, has a significant influence on international commerce, making precise price forecasting critical for investors and traders. In this study, the investigation is made on how deep learning models, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), may be used to solve the problem of excessive price swings in cryptocurrencies like as Bitcoin, Ethereum, Litecoin, and Dogecoin. Previous research has looked at forecasting methods such as ARIMA and Support Vector Machines (SVM), but the findings have fallen short of the promising results obtained by the deep learning models studied in this work. Our major objective is to create strong forecasting models based on LSTM and GRU, with an emphasis on their ability to make credible forecasts for bitcoin values. We find that GRU consistently beats LSTM for the majority of the cryptocurrencies under consideration by comparing their performance using two separate error prediction approaches, namely mean absolute percentage error (MAPE) and root mean square error (RMSE). This study's findings help to enhance prediction methods for understanding and reducing the impact of cryptocurrency price volatility on international commerce. Deep learning algorithms for projecting bitcoin values give significant insights for investors and decision-makers navigating the volatile and ever-changing terrain of the crypto market.
Research on Query Task Fragmentation in the Scenario of Storage and Compute Separation

The cloud-native database is one of the hottest topics in database research. Storage-compute separation architecture with cloud characteristics is designed to process complex and changeable workloads to reduce resource coupling for cloud-native databases. During processing query loads, the computing layer pushes the operator tasks in execution plans generated from loads down to the storage layer through the network. Query optimization techniques such as operator pushdown and parallel computing have been developed and perfected to improve query speed in storage and compute separation architecture. But at the same time, these technologies also increase the complexity of execution plans, leading to an increase in tasks. Moreover, the experiment shows that under the same query load, the cloud-native database executes a significantly greater number of tasks compared to the distributed database. Given the above phenomenon, the concept of query task fragmentation is proposed. From the view of operator pushdown and parallel computing query optimization, the presence of query task fragmentation is verified through experiments. Experimental results based on the TPC-H benchmark demonstrate that, compared to MySQL distributed cluster, the average number of operator tasks in the execution plan, generated through operator pushdown and parallel computing for the cloud-native databases, increases by 20.17% and 39.49%, respectively. Finally, the impact of task fragmentation is analyzed from the perspectives of technical research and the application of cloud-native databases.
Multi-Object Tracking Based on Semantic Information for Autonomous Vehicle

We propose a multi-object tracking method based on semantic information. The related works that focus on the traditional multi-object tracking return a relatively high number of identity switches. In order to solve this problem, we integrate semantic information to analyze the situation of the lost track. At the current frame, the raw images are fed into a universal object detector to obtain the detection results. For multi-object data association, we used a high-low score matching method. For the lost tracks, the predicted position of the track point in the next frame will be output. At the next frame, the raw image is fed into Deeplabv3+ network to obtain the semantic information. The proportion of semantic categories of bounding box is counted. Tracks with the proportion of background category greater than the background threshold are deleted from the track set. Our method is compared with the popular and advance methods on KITTI dataset, and the result of the comparative experiment shows that our method achieves better accuracy than other tracking frameworks.
THE EFFECTS OF AMBIENT LIGHTING IN THE USAGE OF AUGMENTED REALITY

Augmented reality (AR) technology has been adopted in a wide spectrum of applications ranging from education, healthcare to manufacturing. In optical see-through AR devices, the see-through scene illuminated by ambient lighting can have a visible difference in colour and brightness from the virtual image from the AR device. This difference affects the immersion and realism experience. For example, if the ambient lighting is in a warm tone (lower CCT), it may make the higher CCT colors of the AR image appear more muted or washed out. Conversely, if the ambient lighting is cool, warm colors in the AR image may appear more vibrant or saturated. This paper addresses the effects on optical see-through AR image quality under various ambient lighting conditions.
LIGHTING CONDITIONS FOR THE VISIBILITY OF OBJECTS ON THE ROAD SURFACE DURING TUNNEL DRIVING

In this study, we investigated a method to evaluate the visibility of fallen objects on the road by viewing a virtual driving video in a simulated tunnel model captured by a VR180 camera with an HMD. As the results, when the subject was stationary, the subject's sensory evaluation results confirmed that the visibility was almost the same for both the direct view and the HMD. Since the effectiveness of the visibility evaluation with the HMD was confirmed, the visibility of fallen objects on the road was evaluated using a video image of driving in the model tunnel at a converted speed of 80 km/h. When subjects were in a dynamic state, visibility was concentrated around one point, the 'SLIGHTLY LOW' score. This means that subjects recognise before they can see better. Instead of visibility, the distance between the subject and the fallen object is changed at recognition time.
Review of service robot technology acceptance model research

With the continuous progress of science and technology, service robots have gradually penetrated into various industries, including catering, travel, home and medical fields. The rapid development of this technology has attracted wide attention in today's society, involving artificial intelligence, machine learning, and human-computer interaction, and has brought major changes to our lives and work. However, the adoption of new technologies is not always smooth, and the level of acceptance varies. This paper takes the technology acceptance model (TAM) and its derivative model as the theoretical framework to deeply explore the factors that affect the acceptance of service robots. Various factors revealed by TAM technology acceptance model and its derivative model will be discussed in depth respectively. This study will help to reveal all possible factors that affect user acceptance of service robots, such as functional elements, emotional elements and users' own characteristics, for future research.
Security Risk Visualization for Open-Source Software based on Vulnerabilities, Repositories, and Dependencies

Open-source software (OSS) has become a mainstream technology in recent years. However, its secure application requires a thorough understanding of the various security risks involved, e.g., vulnerability and developmental risks. Whenever a new vulnerability is identified in an OSS in use, the security administrator must update it. On the other hand, if an OSS is not under active development, an alternative OSS should be considered because newly identified vulnerabilities may not be fixed. Further, besides the OSSs directly in use, their dependencies must also be csonsidered. Thus, security administrators require a specialized method to analyze vulnerability and developmental risks of OSSs, considering their dependencies. For this purpose, a method is proposed in this study that identifies security risks in an OSS by extracting, linking, and visualizing its vulnerability, developmental, and dependency information using vulnerability databases and packaging management tools. Its performance and processing time are evaluated by applying it to the visualization of an OSS with additional vulnerability information and different scores representing development information.
Research on Situation Awareness Strategy of Source-Network-Load-Storage System based on Metaverse and Blockchain

The metaverse of source-network-load-storage system generates the mirror of the physical entity based on digital twin and constructs an information exchange system based on blockchain. The construction of metaverse platform of source-network-load-storage system consists of the device layer, the object layer, the virtual layer and the application layer. The cloud computing servers, the edge gateway servers and human-computer interaction interfaces are set for the different layers of metaverse platform. The situation understanding includes identifying attack activities and identifying attack sources which can use situation guidance for a new round of situation prediction. BP neural network will be used to the operation situation awareness of source-network-load-storage system. In the source-network-load-storage system, the node types on the blockchain mainly include wind farm, photovoltaic station, power grid company, user load, energy storage and regulatory agency. The process of operation situation awareness of source-network-load-storage system on the blockchain in metaverse is presented to achieve the target of situation awareness as well as safe and stable operation of source-network-load-storage system. Different blockchain channels in metaverse platform are used for multiple source-network-load-storage systems to exchange and share information.
Developing Video Image Processing Technique for Vehicle Speed Estimations

Two-thirds of the injury regarding road traffic deaths are being reported in the age group 15–44 years. The number is ever increasing regarding to the fatal road accidents. About 16 deaths, as well as 58 injuries on the road, has been happening per hour in India. Vehicle speed checking is pivotal for approving pace limit laws in India. It similarly tells the traffic conditions of the noticed piece of the road of interest. In this examination work, we present a more affordable system that uses consistently use the camcorder and another figuring that utilizes the computerized picture handling and camera optics to normally and exactly recognize vehicle speed logically. The computation requires only a singular camcorder and a locally accessible PC to work and can simultaneously distinguish vehicle speeds in various ways with better precision. This strategy diminishes the cost at an extreme level while giving a broad proportion of exactness. The trade-off between these two characteristics can be legitimized with a high drop in costs and to a sensible compromise inaccuracy. However, the precision can be improved and scaled additional time and persistently with better figures and techniques as the advancement around improves better.
Prediction of Web Page Relevance in Focused Crawling Using Artificial Neural Networks

In the rapidly expanding digital ecosystem, efficient web crawling is paramount to extract relevant information from the vast expanse of the internet. This research addresses the challenge of predicting web page relevance in focused crawling using Artificial Neural Networks (ANNs). The study leverages a curated dataset of web pages, annotated for their relevance to specific domains. A multi-layer perceptron architecture was employed for the ANN, with features extracted from the web pages serving as inputs. The model was trained and validated using a split of 70% training and 30% testing data. Experimental results indicate that the ANN achieved an accuracy of 93.20%, outperforming traditional methods by a margin of 8%. This demonstrates the potential of ANNs in enhancing the efficiency of focused web crawlers, ensuring more accurate data extraction in less time. The findings pave the way for integrating deep learning techniques into web crawling systems, optimizing them for the evolving digital landscape.
Solar Radiation Prediction Based on Hybrid Machine Learning Technique

Solar radiation forecasting is crucial for optimizing solar power generation systems and integrating solar energy into the energy grid. Traditional physical models have limitations in accuracy due to the complexity of atmospheric processes. This research presents a novel hybrid machine learning approach that combines Random Forest and XGBoost algorithms for solar radiation prediction. The proposed hybrid model leverages the strengths of both the algorithms to enhance prediction accuracy. Simulation results demonstrate the superior performance of the hybrid model compared to conventional models, offering a promising solution for efficient solar energy utilization and a greener future.
Digit Recognition by the Implementation of Supervised Learning Using a Convolutional Neural Network

The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In recent years, there has been a notable enhancement in our technological capabilities due to the progress made in internet accessibility and technology. Deep learning algorithms have emerged as valuable computational tools for addressing several challenges, including the task of digit recognition. The utilization of convolutional neural networks in the task of digit recognition is driven by their ability to acquire hierarchical representations from picture data. This scholarly article provides a comprehensive examination of the application of Convolutional Neural Networks (CNNs) in the field of machine learning for the purpose of digit recognition. This research examines the architectural components of Convolutional Neural Networks (CNNs), the training procedures employed, and the various methodologies employed for the purpose of digit recognition. The paper also provides an overview of the different datasets used for training and testing CNNs for digit recognition and a discussion of the results obtained from various studies.
A Discrete-Time Image Hiding Algorithm Transform Using Wavelet and SHA-512

Steganography is an ancient technique used to conceal information within another medium, enhancing security alongside cryptographic methods. This paper's steganography approach revolves around concealing data within the skin region of a cover image converted from RGB to YCbCr colour space. The YCbCr colour model facilitates skin tone detection, employing its components Yellow, Chromatic Blue, and Chromatic Red. The secret information is first encrypted using the SHA-512 algorithm for robust data hiding. Next, a frequency domain approach, specifically the Discrete Wavelet Transform (DWT), is employed for embedding, proving more effective than the Discrete Cosine Transform (DCT). The key idea is to use an object-oriented steganography mechanism that focuses on tracking skin tone objects within the image, thereby enhancing security. The secret encrypted data is embedded in one of the high-frequency sub-bands of DWT, specifically within skin pixels in that sub-band. This approach demonstrates higher security levels through experimentation while maintaining satisfactory PSNR (Peak Signal-to-Noise Ratio) values. By adopting this technique, the paper highlights an efficient and secure way to achieve hidden communication without compromising the cover image's visual quality.”
A Mixture Cauchy Kernel Based Estimation Method for INS/GNSS under GNSS-Challenged Environment

Due to the existence of a challenged environment for intelligent vehicle position such as non-Gaussian measurement noises and sudden changes of system status, the robust forecasting-aided state estimation is pivotal for non-linear inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system stability. In order to enhance the robustness of the conventional non-linear method utilized in the INS/GNSS system, this study introduces a robust Cubature Kalman filter (CKF) that relies on the mixture Cauchy kernel (MCK). The utilization of the greatest MCK correntropy is employed as a substitute for the mean square error loss inside a reduced CKF framework. One advantage of employing this approach is the integration of the MCK and CKF, which has been developed in the context of robust information theoretic learning to address non-Gaussian interference in GNSS data, with the CKF, which excels in handling strong model nonlinearities. Additionally, this approach ensures the computational efficiency of the algorithm. Numerical simulation results carried out on INS/GNSS nonlinearity systems validate the efficacy of the proposed methods for state estimation under various types of measurement.