Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"Correlation analysis of multifractal stock price fluctuations based on partition function","authors":"Huan Wang,&nbsp;Wei Song","doi":"10.1016/j.jksuci.2024.102233","DOIUrl":"10.1016/j.jksuci.2024.102233","url":null,"abstract":"<div><div>Studying the correlation analysis of stock price fluctuations helps to understand market dynamics better and improve the scientific nature of investment decisions and risk management capabilities. Most existing methods use multifractals to explore the correlation between different economic entities. However, the study of multifractals fails to fully consider the weight of each entity’s impact on the market, resulting in an inaccurate description of the overall market dynamics. To address this problem, this paper creatively proposes a weighted multifractal analysis method (WMA). The correlation analysis of government regulation, market supply and demand, and stock price index is performed using the data of A-share listed companies in Shenzhen and Shanghai as samples. First, we consider the amplitude fluctuation information the signal carries and weigh the partition function according to the proportion of variance in the segment for different amplitude changes. Secondly, we derive the theoretical analytical form of the classical multifractal model (SMA) of the scaling indicator under WMA. Finally, through numerical simulation experiments, it is confirmed that WMA is equally effective as SMA. In addition, the re-fractal correlation analysis of real financial time series also confirms that WMA can effectively utilize the amplitude fluctuation information in the series and outperforms the classical SMA method in distinguishing different signals.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102233"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hierarchical and secure approach for automotive firmware upgrades","authors":"Feng Luo ,&nbsp;Zhihao Li ,&nbsp;Jiajia Wang ,&nbsp;Cheng Luo ,&nbsp;Hongqian Liu ,&nbsp;Dengcheng Liu","doi":"10.1016/j.jksuci.2024.102258","DOIUrl":"10.1016/j.jksuci.2024.102258","url":null,"abstract":"<div><div>With the development of intelligent and connected vehicles, the expansion of software necessitates an increased significance and frequency of automotive firmware upgrades. The abundance of potential attack vectors and valuable data renders these upgrades enticing targets for attackers. However, the prevailing security services used for automotive firmware upgrades are no longer sufficient to meet security requirements. Hence, this paper proposes a Secure Automotive Firmware Upgrade Approach (SAFUA), aimed at enhancing authentication and communication security during automotive firmware upgrades. To address the heterogeneous performance of in-vehicle nodes and diverse application contexts, this approach introduces multiple authentication modes tailored to various upgrade scenarios. Moreover, hierarchical authentication and secure communication strategies are designed to achieve a balance between security and efficiency requirements. Consolidating these methodologies, a standardized automotive firmware upgrade process is delineated. Formal and informal verification of the proposed approach is conducted to attest its security efficacy. Furthermore, a simulated vehicular environment is constructed to evaluate the temporal and spatial efficiency of the approach across diverse bus and device configurations. The results confirm the adaptability of the secure upgrade approach outlined herein to the automotive firmware upgrade landscape, offering robust security alongside enhanced upgrade efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102258"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"T-SRE: Transformer-based semantic Relation extraction for contextual paraphrased plagiarism detection","authors":"Pon Abisheka ,&nbsp;C. Deisy ,&nbsp;P. Sharmila","doi":"10.1016/j.jksuci.2024.102257","DOIUrl":"10.1016/j.jksuci.2024.102257","url":null,"abstract":"<div><div>Plagiarism has become a pervasive issue in academics and professionals to safeguard academic integrity and intellectual property rights. The escalating sophistication of plagiarized content through semantic manipulation and structural reorganization poses significant challenges to existing detection systems that rely primarily on lexical similarity measures. The proposed T-SRE (Transformer-based Semantic Relation Extraction), a novel framework addresses the limitations of traditional n-gram and string-matching approaches by leveraging deep semantic analysis. The proposed framework combines Dependency Parsing (DP) for syntactic relationship mapping and Named Entity Recognition (NER) for contextual entity identification, augmented by a transformer-based neural network that captures long-range contextual dependencies. This learning methodology incorporates three key components: a position-aware word reordering algorithm, Levenshtein distance metric for structural similarity, and contextual word embeddings for semantic preservation detection. The proposed T-SRE enhances text structure recognition by combining position-aware reordering with semantic preservation through ensemble learning. The system implements a hierarchical classification scheme that quantifies plagiarism severity through a four-tier taxonomy: heavy, low, non-plagiarized and verbatim copy. The Udacity benchmark dataset showcases the model’s superior detection capabilities, achieving 92% precision, 89% recall, and an F1-score of 90.5%, particularly in lightweight textual modifications.The framework achieves a granularity score of 1.28, outperforming existing approaches.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102257"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Audio analysis with convolutional neural networks and boosting algorithms tuned by metaheuristics for respiratory condition classification","authors":"Safet Purkovic ,&nbsp;Luka Jovanovic ,&nbsp;Miodrag Zivkovic ,&nbsp;Milos Antonijevic ,&nbsp;Edin Dolicanin ,&nbsp;Eva Tuba ,&nbsp;Milan Tuba ,&nbsp;Nebojsa Bacanin ,&nbsp;Petar Spalevic","doi":"10.1016/j.jksuci.2024.102261","DOIUrl":"10.1016/j.jksuci.2024.102261","url":null,"abstract":"<div><div>In contemporary medical research, respiratory disorders have become a primary focus. Improving patient outcomes for any medical condition largely depends on early identification and prompt treatment. Traditionally, medical professionals diagnose respiratory diseases by auscultating a patient’s breathing. However, this method has inherent limitations, as it may not enable physicians to accurately identify every respiratory condition. This study explores the potential of using convolutional neural networks (CNNs) in conjunction with audio analysis for the identification of respiratory problems. This work proses a novel two-tier framework that integrates CNNs with extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) models to classify respiratory conditions. Additionally, modern optimization techniques are employed to enhance classification efficiency, recognizing the significant impact that appropriate hyperparameter tuning has on machine learning (ML) and deep learning (DL) performance. This research introduces a modified version of particle swarm optimization (PSO) tailored to meet the specific needs of ML and DL tuning. The proposed approach is validated using a real-world clinical dataset. Two studies, both based on mel spectrograms of patient breathing patterns, were conducted: the first aimed at determining whether patients have respiratory conditions (binary classification), while the second employed the same data structure for multi-class classification. In both scenarios, advanced optimizers were utilized to optimize model architecture and training settings. Under identical testing conditions, the proposed PSO metaheuristic achieved an accuracy of 98.14<span><math><mtext>%</mtext></math></span> for respiratory condition detection in binary classification and a slightly lower accuracy of 81.25<span><math><mtext>%</mtext></math></span> for specific condition identification in multi-class classification.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102261"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced secure lossless image steganography using invertible neural networks","authors":"Weida Chen ,&nbsp;Weizhe Chen","doi":"10.1016/j.jksuci.2024.102259","DOIUrl":"10.1016/j.jksuci.2024.102259","url":null,"abstract":"<div><div>Image steganography is a technique that embeds secret data into cover images in an imperceptible manner, ensuring that the original data can be recovered by the receiver without arousing suspicion. The key challenges currently faced by image steganography are capacity, invisibility, and security. We suggest an invertible neural network-based image steganography technique to concurrently address these three issues. To achieve better invisibility, we adopt a method that avoids the loss of information, thereby preventing ill-posed problems. The learning cost during image embedding can be reduced by only fitting part of the color channels in order to address the issue of high capacity. Additionally, we introduce the concept of a key to constrain the embedding process of the secret information, significantly enhancing the security of the hidden data. According to our experimental results, our method outperforms other image steganography algorithms on DIV2K, COCO, and ImageNet datasets, achieving perfect recovery of the secret images, its PSNR and SSIM can reach the theoretical maximum values.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102259"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Binocular camera-based visual localization with optimized keypoint selection and multi-epipolar constraints","authors":"Guanyuan Feng,&nbsp;Yu Liu,&nbsp;Weili Shi,&nbsp;Yu Miao","doi":"10.1016/j.jksuci.2024.102228","DOIUrl":"10.1016/j.jksuci.2024.102228","url":null,"abstract":"<div><div>In recent years, visual localization has gained significant attention as a key technology for indoor navigation due to its outstanding accuracy and low deployment costs. However, it still encounters two primary challenges: the requirement for multiple database images to match the query image and the potential degradation of localization precision resulting from the keypoints clustering and mismatches. In this research, a novel visual localization framework based on a binocular camera is proposed to estimate the absolute positions of the query camera. The framework integrates three core methods: the multi-epipolar constraints-based localization (MELoc) method, the Optimal keypoint selection (OKS) method, and a robust measurement method. MELoc constructs multiple geometric constraints to enable absolute position estimation with only a single database image, while OKS and the robust measurement method further enhance localization accuracy by refining the precision of these geometric constraints. Experimental results demonstrate that the proposed system consistently outperforms existing visual localization systems across various scene scales, database sampling intervals, and lighting conditions</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102228"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly detection in sensor data via encoding time series into images","authors":"Jidong Ma (继东) ,&nbsp;Hairu Wang (王海茹)","doi":"10.1016/j.jksuci.2024.102232","DOIUrl":"10.1016/j.jksuci.2024.102232","url":null,"abstract":"<div><div>Detecting anomalies in multivariate time series data is crucial for maintaining the optimal functionality of control system equipment. While existing research has made significant strides in this area, the increasing complexity of industrial environments poses challenges in accurately capturing the interactions between variables. Therefore, this paper introduces an innovative anomaly detection approach that extends one-dimensional time series into two-dimensions to capture the spatial correlations within the data. Unlike traditional approaches, we utilize the Gramian Angular Field to encode the correlations between different sensors at specific time points into images, enabling precise learning of spatial information across multiple variables. Subsequently, we construct an adversarial generative model to accurately identify anomalies at the pixel level, facilitating precise localization of abnormal points. We evaluate our method using five open-source datasets from various fields. Our method outperforms state-of-the-art anomaly detection techniques across all datasets, showcasing its superior performance. Particularly, our method achieves a 11.5% increase in F1 score on the high-dimensional WADI dataset compared to the baseline method. Additionally, we conduct thorough effectiveness analysis, parameter impact experiments, significant statistical analysis, and burden analysis, confirming the efficacy of our approach in capturing both the temporal dynamics and spatial relationships inherent in multivariate time series data.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102232"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semantic similarity on multimodal data: A comprehensive survey with applications","authors":"Baha Ihnaini ,&nbsp;Belal Abuhaija ,&nbsp;Ebenezer Atta Mills ,&nbsp;Massudi Mahmuddin","doi":"10.1016/j.jksuci.2024.102263","DOIUrl":"10.1016/j.jksuci.2024.102263","url":null,"abstract":"<div><div>Recently, the revival of the semantic similarity concept has been featured by the rapidly growing artificial intelligence research fueled by advanced deep learning architectures enabling machine intelligence using multimodal data. Thus, semantic similarity in multimodal data has gained substantial attention among researchers. However, the existing surveys on semantic similarity measures are restricted to a single modality, mainly text, which significantly limits the capability to understand the intelligence of real-world application scenarios. This study critically reviews semantic similarity approaches by shortlisting 223 vital articles from the leading databases and digital libraries to offer a comprehensive and systematic literature survey. The notable contribution is to illuminate the evolving landscape of semantic similarity and its crucial role in understanding, interpreting, and extracting meaningful information from multimodal data. Primarily, it highlights the challenges and opportunities inherent in different modalities, emphasizing the significance of advancements in cross-modal and multimodal semantic similarity approaches with potential application scenarios. Finally, the survey concludes by summarizing valuable future research directions. The insights provided in this survey improve the understanding and pave the way for further innovation by guiding researchers in leveraging the strength of semantic similarity for an extensive range of real-world applications.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102263"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An oversampling FCM-KSMOTE algorithm for imbalanced data classification","authors":"Hongfang Zhou ,&nbsp;Jiahao Tong ,&nbsp;Yuhan Liu ,&nbsp;Kangyun Zheng ,&nbsp;Chenhui Cao","doi":"10.1016/j.jksuci.2024.102248","DOIUrl":"10.1016/j.jksuci.2024.102248","url":null,"abstract":"<div><div>In recent years, imbalanced data classification has emerged as a challenging task. To address this issue, we propose a novel oversampling method named FCM-KSMOTE. The algorithm initially performs a density-based fuzzy clustering on the data, then iterates to partition regions and perform oversampling inside each cluster. Secondly, it merges the clusters and conducts noise detection to obtain a balanced dataset. Finally, we conducted the experiments on 19 public datasets and 3 synthetic datasets. Six evaluation metrics of Recall, Accuracy, G-mean, Specificity, AUC and F1-Score were used in the experiments. The experimental results demonstrate that our method can significantly improve the recognition rate of the minority class while maintaining high accuracy for the majority class. Particularly with the RF classifier, our method ranks first in all evaluation metrics, with a Recall difference of up to 0.2 compared to the least performing method, demonstrating its substantial performance advantage.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102248"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning-based local path planning in dynamic environments for mobile robot","authors":"Bodong Tao,&nbsp;Jae-Hoon Kim","doi":"10.1016/j.jksuci.2024.102254","DOIUrl":"10.1016/j.jksuci.2024.102254","url":null,"abstract":"<div><div>Path planning for robots in dynamic environments is a challenging task, as it requires balancing obstacle avoidance, trajectory smoothness, and path length during real-time planning.This paper proposes an algorithm called Adaptive Soft Actor–Critic (ASAC), which combines the Soft Actor–Critic (SAC) algorithm, tile coding, and the Dynamic Window Approach (DWA) to enhance path planning capabilities. ASAC leverages SAC with an automatic entropy adjustment mechanism to balance exploration and exploitation, integrates tile coding for improved feature representation, and utilizes DWA to define the action space through parameters such as target heading, obstacle distance, and velocity In this framework, the action space is defined by DWA’s three weighting parameters: target heading deviation, distance to the nearest obstacle, and velocity. To facilitate the learning process, a non-sparse reward function is designed, incorporating factors such as Time-to-Collision (TTC), heading, and velocity. To validate the effectiveness of the algorithm, experiments were conducted in four different environments, and the algorithm was evaluated based on metrics such as trajectory deviation, smoothness, and time to reach the end point. The results demonstrate that ASAC outperforms existing algorithms in terms of trajectory smoothness, arrival time, and overall adaptability across various scenarios, effectively enabling path planning in dynamic environments.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102254"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}