Pub Date : 2024-12-01DOI: 10.1016/j.jksuci.2024.102255
Liangdong Qu , Jingkun Fan
Unmanned combat aerial vehicles (UCAV) path planning in complex environments demands a substantial number of path points to determine feasible paths. Establishing an effective flight path for UCAVs requires numerous path points to account for fuel constraints, artillery threats, and radar avoidance. This increase in path points raises the dimensionality of the problem, which in turn degrades algorithm performance. To mitigate this issue, a double-layer coding (DLC) model is utilized to remove redundant path points, consequently lowering computational complexity and operational difficulties. Meanwhile, this paper introduces a novel enhanced sparrow search algorithm (MESSA) based on multi-strategy for UCAV path planning. The MESSA incorporates a novel dynamic fitness regulation learning strategy (DFRL), a random differential learning strategy (RDL), an elite example equilibrium learning strategy (EEEL), a dynamic elimination and regeneration strategy based on the elite example (DERE), and quadratic interpolation (QI). Furthermore, MESSA is compared against 11 state-of-the-art algorithms, demonstrating exceptional optimization performance and robustness. Additionally, the combination of MESSA with the DLC model (DLC-MESSA) is applied to solve the UCAV path planning problem. The experimental results from five complex environments indicate that DLC-MESSA outperforms other algorithms in 80% of the cases by achieving the lowest average cost, thereby demonstrating its superior robustness and computational efficiency.
{"title":"Unmanned combat aerial vehicle path planning in complex environment using multi-strategy sparrow search algorithm with double-layer coding","authors":"Liangdong Qu , Jingkun Fan","doi":"10.1016/j.jksuci.2024.102255","DOIUrl":"10.1016/j.jksuci.2024.102255","url":null,"abstract":"<div><div>Unmanned combat aerial vehicles (UCAV) path planning in complex environments demands a substantial number of path points to determine feasible paths. Establishing an effective flight path for UCAVs requires numerous path points to account for fuel constraints, artillery threats, and radar avoidance. This increase in path points raises the dimensionality of the problem, which in turn degrades algorithm performance. To mitigate this issue, a double-layer coding (DLC) model is utilized to remove redundant path points, consequently lowering computational complexity and operational difficulties. Meanwhile, this paper introduces a novel enhanced sparrow search algorithm (MESSA) based on multi-strategy for UCAV path planning. The MESSA incorporates a novel dynamic fitness regulation learning strategy (DFRL), a random differential learning strategy (RDL), an elite example equilibrium learning strategy (EEEL), a dynamic elimination and regeneration strategy based on the elite example (DERE), and quadratic interpolation (QI). Furthermore, MESSA is compared against 11 state-of-the-art algorithms, demonstrating exceptional optimization performance and robustness. Additionally, the combination of MESSA with the DLC model (DLC-MESSA) is applied to solve the UCAV path planning problem. The experimental results from five complex environments indicate that DLC-MESSA outperforms other algorithms in 80% of the cases by achieving the lowest average cost, thereby demonstrating its superior robustness and computational efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102255"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744902","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}
Pub Date : 2024-12-01DOI: 10.1016/j.jksuci.2024.102257
Pon Abisheka , C. Deisy , P. Sharmila
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.
{"title":"T-SRE: Transformer-based semantic Relation extraction for contextual paraphrased plagiarism detection","authors":"Pon Abisheka , C. Deisy , 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}
Pub Date : 2024-11-23DOI: 10.1016/j.jksuci.2024.102256
Guijin Han , Yuanzheng Zhang , Mengchun Zhou
Traditional feature matching algorithms often struggle with poor performance in scenarios involving local detail deformations under varying perspectives. Additionally, traditional optimal seamline search-based image stitching algorithms tend to overlook structural and texture information, resulting in ghosting and visible seams. To address these issues, this paper proposes an image stitching algorithm based on a two-stage optimal seamline search. The algorithm leverages a Homography Network as the foundation, incorporating a homography detail-aware network (HDAN) for feature point matching. By introducing a cost volume in the feature matching layer, the algorithm enhances the description of local detail deformation relationships, thereby improving feature matching performance under different perspectives. The two-stage optimal seamline search algorithm designed for image fusion introduces gradient and structural similarity features on top of traditional color-based optimal seamline search algorithms. The algorithm steps include: (1) Searching for structurally similar regions, i.e., high-frequency regions in high-gradient images, and using a color-based graph cut algorithm to search for seamlines within all high-frequency regions, excluding horizontal seamlines; (2) Using a dynamic programming algorithm to complete each vertical seamline, where the pixel energy is comprehensively calculated based on its differences in color and gradient with the surrounding area. The complete seamline energies are then calculated using color, gradient, and structural similarity differences within the seamline neighborhood, and the seamline with the minimum energy is selected as the optimal seamline. A simulation experiment was conducted using 30 image pairs from the UDIS-D dataset (Unsupervised Deep Image Stitching Dataset). The results demonstrate significant improvements in PSNR and SSIM metrics compared to other image stitching algorithms, with PSNR improvements ranging from 5.63% to 11.25% and SSIM improvements ranging from 11.09% to 24.54%, confirming the superiority of this algorithm in image stitching tasks. The proposed image stitching algorithm based on two-stage optimal seamline search, whether evaluated through subjective visual perception or objective data comparison, outperforms other algorithms by enhancing the natural transition of seamlines in terms of structure and texture, reducing ghosting and visible seams in stitched images.
{"title":"Image stitching algorithm based on two-stage optimal seam line search","authors":"Guijin Han , Yuanzheng Zhang , Mengchun Zhou","doi":"10.1016/j.jksuci.2024.102256","DOIUrl":"10.1016/j.jksuci.2024.102256","url":null,"abstract":"<div><div>Traditional feature matching algorithms often struggle with poor performance in scenarios involving local detail deformations under varying perspectives. Additionally, traditional optimal seamline search-based image stitching algorithms tend to overlook structural and texture information, resulting in ghosting and visible seams. To address these issues, this paper proposes an image stitching algorithm based on a two-stage optimal seamline search. The algorithm leverages a Homography Network as the foundation, incorporating a homography detail-aware network (HDAN) for feature point matching. By introducing a cost volume in the feature matching layer, the algorithm enhances the description of local detail deformation relationships, thereby improving feature matching performance under different perspectives. The two-stage optimal seamline search algorithm designed for image fusion introduces gradient and structural similarity features on top of traditional color-based optimal seamline search algorithms. The algorithm steps include: (1) Searching for structurally similar regions, i.e., high-frequency regions in high-gradient images, and using a color-based graph cut algorithm to search for seamlines within all high-frequency regions, excluding horizontal seamlines; (2) Using a dynamic programming algorithm to complete each vertical seamline, where the pixel energy is comprehensively calculated based on its differences in color and gradient with the surrounding area. The complete seamline energies are then calculated using color, gradient, and structural similarity differences within the seamline neighborhood, and the seamline with the minimum energy is selected as the optimal seamline. A simulation experiment was conducted using 30 image pairs from the UDIS-D dataset (Unsupervised Deep Image Stitching Dataset). The results demonstrate significant improvements in PSNR and SSIM metrics compared to other image stitching algorithms, with PSNR improvements ranging from 5.63% to 11.25% and SSIM improvements ranging from 11.09% to 24.54%, confirming the superiority of this algorithm in image stitching tasks. The proposed image stitching algorithm based on two-stage optimal seamline search, whether evaluated through subjective visual perception or objective data comparison, outperforms other algorithms by enhancing the natural transition of seamlines in terms of structure and texture, reducing ghosting and visible seams in stitched images.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102256"},"PeriodicalIF":5.2,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723733","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}
Pub Date : 2024-11-22DOI: 10.1016/j.jksuci.2024.102250
Xiaolan Wen , Anwen Zhang , Chuan Lin , Xintao Pang
Technology for automatic segmentation plays a crucial role in the early diagnosis and treatment of ColoRectal Cancer (CRC). Existing polyp segmentation methods often focus on advanced feature extraction while neglecting detailed low-level features, This somewhat limits the enhancement of segmentation performance. This paper proposes a new technique called the Cascaded Refinement Network (CRNet), designed to improve polyp segmentation performance by combining low-level and high-level features through a cascaded contextual network structure. To accurately capture the morphological variations of polyps and enhance the clarity of segmentation boundaries, we have designed the Multi-Scale Feature Optimization (MFO) module and the Contextual Edge Guidance (CEG) module. Additionally, to further enhance feature fusion and utilization, we introduced the Cascaded Local Feature Fusion (CLFF) module, which effectively integrates cross-layer correlations, allowing the network to understand complex polyp structures better. By conducting a large number of experiments, our model achieved a 0.3% and 3.1% higher mDice score than the latest MMFIL-Net in the two main datasets of Kvasir-SEG and CVC-ClinicDB, respectively. Ablation studies show that MFO improves the baseline score by 4%, and the network without CLFF and CEG results in a reduction of 2.4% and 1.7% in mDice scores, respectively. This further validates the contribution of each module to the polyp segmentation performance. CRNet enhances model performance through the introduction of multiple modules but also increases model complexity. Future work will explore how to reduce computational complexity and improve inference speed while maintaining high performance. The source code for this paper can be found at https://github.com/l1986036/CRNet.
{"title":"CRNet: Cascaded Refinement Network for polyp segmentation","authors":"Xiaolan Wen , Anwen Zhang , Chuan Lin , Xintao Pang","doi":"10.1016/j.jksuci.2024.102250","DOIUrl":"10.1016/j.jksuci.2024.102250","url":null,"abstract":"<div><div>Technology for automatic segmentation plays a crucial role in the early diagnosis and treatment of ColoRectal Cancer (CRC). Existing polyp segmentation methods often focus on advanced feature extraction while neglecting detailed low-level features, This somewhat limits the enhancement of segmentation performance. This paper proposes a new technique called the Cascaded Refinement Network (CRNet), designed to improve polyp segmentation performance by combining low-level and high-level features through a cascaded contextual network structure. To accurately capture the morphological variations of polyps and enhance the clarity of segmentation boundaries, we have designed the Multi-Scale Feature Optimization (MFO) module and the Contextual Edge Guidance (CEG) module. Additionally, to further enhance feature fusion and utilization, we introduced the Cascaded Local Feature Fusion (CLFF) module, which effectively integrates cross-layer correlations, allowing the network to understand complex polyp structures better. By conducting a large number of experiments, our model achieved a 0.3% and 3.1% higher mDice score than the latest MMFIL-Net in the two main datasets of Kvasir-SEG and CVC-ClinicDB, respectively. Ablation studies show that MFO improves the baseline score by 4%, and the network without CLFF and CEG results in a reduction of 2.4% and 1.7% in mDice scores, respectively. This further validates the contribution of each module to the polyp segmentation performance. CRNet enhances model performance through the introduction of multiple modules but also increases model complexity. Future work will explore how to reduce computational complexity and improve inference speed while maintaining high performance. The source code for this paper can be found at <span><span>https://github.com/l1986036/CRNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102250"},"PeriodicalIF":5.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142723734","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}
Pub Date : 2024-11-20DOI: 10.1016/j.jksuci.2024.102251
Khandakar Md Shafin , Saha Reno
In order to maintain the value of the national currency and control foreign debt, central banks are vital to the management of a nation’s foreign exchange reserves. These reserves, however, are vulnerable to a variety of hazards, including as money laundering, fraud, theft, and cyberattacks. These are issues that traditional financial systems frequently face because of their vulnerabilities and inefficiency. Using modern innovations in a blockchain-based solution can help tackle these serious issues. To protect data privacy, the Microsoft SEAL library is utilized for homomorphic encryption (FHE). For the development of smart contracts, Solidity is employed within the Ethereum blockchain ecosystem. Additionally, Amazon Web Services (AWS) is leveraged to provide a scalable and powerful infrastructure to support our solution. To guarantee safe and effective transaction validation, our method incorporates a hybrid consensus process that combines Proof of Authority (PoA) with Byzantine Fault Tolerance (BFT). The administration of foreign exchange reserves by central banks is made more secure, transparent, and operationally efficient by this all-inclusive approach.
为了保持本国货币的价值和控制外债，中央银行对国家外汇储备的管理至关重要。然而，这些储备容易受到各种危害的影响，包括洗钱、欺诈、盗窃和网络攻击。这些都是传统金融系统因其脆弱性和低效率而经常面临的问题。在基于区块链的解决方案中使用现代创新技术有助于解决这些严重问题。为了保护数据隐私，微软 SEAL 库被用于同态加密（FHE）。为了开发智能合约，在以太坊区块链生态系统中使用了 Solidity。此外，亚马逊网络服务（AWS）为支持我们的解决方案提供了可扩展的强大基础设施。为了保证安全有效的交易验证，我们的方法采用了混合共识流程，将权威证明（PoA）与拜占庭容错（BFT）相结合。通过这种包罗万象的方法，中央银行对外汇储备的管理变得更加安全、透明和高效。
{"title":"Enhancing foreign exchange reserve security for central banks using Blockchain, FHE, and AWS","authors":"Khandakar Md Shafin , Saha Reno","doi":"10.1016/j.jksuci.2024.102251","DOIUrl":"10.1016/j.jksuci.2024.102251","url":null,"abstract":"<div><div>In order to maintain the value of the national currency and control foreign debt, central banks are vital to the management of a nation’s foreign exchange reserves. These reserves, however, are vulnerable to a variety of hazards, including as money laundering, fraud, theft, and cyberattacks. These are issues that traditional financial systems frequently face because of their vulnerabilities and inefficiency. Using modern innovations in a blockchain-based solution can help tackle these serious issues. To protect data privacy, the Microsoft SEAL library is utilized for homomorphic encryption (FHE). For the development of smart contracts, Solidity is employed within the Ethereum blockchain ecosystem. Additionally, Amazon Web Services (AWS) is leveraged to provide a scalable and powerful infrastructure to support our solution. To guarantee safe and effective transaction validation, our method incorporates a hybrid consensus process that combines Proof of Authority (PoA) with Byzantine Fault Tolerance (BFT). The administration of foreign exchange reserves by central banks is made more secure, transparent, and operationally efficient by this all-inclusive approach.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102251"},"PeriodicalIF":5.2,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705879","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}
Pub Date : 2024-11-19DOI: 10.1016/j.jksuci.2024.102249
Muhammad Sheraz , Teong Chee Chuah , Kashif Sultan , Manzoor Ahmed , It Ee Lee , Saw Chin Tan
Cache-enabled Device-to-Device (D2D) communications is an effective way to improve data sharing. User Equipment (UE)-level caching holds the potential to reduce the data traffic burden on the core network. Licensed spectrum is utilized for D2D communications, but due to spectrum scarcity, exploring unlicensed spectrum is essential to enhance network capacity. In this paper, we propose caching at the UE-level and exploit both licensed and unlicensed spectrum for optimizing throughput. First, we propose a reinforcement learning-based data caching scheme leveraging an actor–critic network to improve cache-enabled D2D communications. Besides, licensed and unlicensed spectrum are devised for D2D communications considering interference from existing cellular and Wi-Fi users. A duty cycle-based unlicensed spectrum access algorithm is employed, guaranteeing the Signal-to-Interference and Noise Ratio (SINR) required by the users. The unlicensed spectrum is prone to data packets collisions. Therefore, Request-to-Send/Clear-to-Send (RTS/CTS) mechanism is utilized in conjunction with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to alleviate both the interference and packets collision problems of the unlicensed spectrum. Extensive simulations are performed to analyze the performance gain of our proposed scheme compared to the benchmarks under different network scenarios. The obtained results demonstrate that our proposed scheme possesses the potential to optimize network performance.
{"title":"Improving cache-enabled D2D communications using actor–critic networks over licensed and unlicensed spectrum","authors":"Muhammad Sheraz , Teong Chee Chuah , Kashif Sultan , Manzoor Ahmed , It Ee Lee , Saw Chin Tan","doi":"10.1016/j.jksuci.2024.102249","DOIUrl":"10.1016/j.jksuci.2024.102249","url":null,"abstract":"<div><div>Cache-enabled Device-to-Device (D2D) communications is an effective way to improve data sharing. User Equipment (UE)-level caching holds the potential to reduce the data traffic burden on the core network. Licensed spectrum is utilized for D2D communications, but due to spectrum scarcity, exploring unlicensed spectrum is essential to enhance network capacity. In this paper, we propose caching at the UE-level and exploit both licensed and unlicensed spectrum for optimizing throughput. First, we propose a reinforcement learning-based data caching scheme leveraging an actor–critic network to improve cache-enabled D2D communications. Besides, licensed and unlicensed spectrum are devised for D2D communications considering interference from existing cellular and Wi-Fi users. A duty cycle-based unlicensed spectrum access algorithm is employed, guaranteeing the Signal-to-Interference and Noise Ratio (SINR) required by the users. The unlicensed spectrum is prone to data packets collisions. Therefore, Request-to-Send/Clear-to-Send (RTS/CTS) mechanism is utilized in conjunction with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) to alleviate both the interference and packets collision problems of the unlicensed spectrum. Extensive simulations are performed to analyze the performance gain of our proposed scheme compared to the benchmarks under different network scenarios. The obtained results demonstrate that our proposed scheme possesses the potential to optimize network performance.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102249"},"PeriodicalIF":5.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705873","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}
Pub Date : 2024-11-19DOI: 10.1016/j.jksuci.2024.102247
Zhixin Ren, Yimin Yu, Enhua Yan, Taowei Chen
To enhance the security of ciphertext-policy attribute-based encryption (CP-ABE) and achieve fully distributed key generation (DKG), this paper proposes a ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof based on Layer-2 and multi-authority CP-ABE. Firstly, we enhance the system into two layers and construct a Layer-2 distributed key management service framework. This framework improves system efficiency and scalability while reducing costs. Secondly, we design the proof of trust contribution (PoTC) consensus algorithm to elect high-trust nodes responsible for DKG and implement an incentive mechanism for key computation through smart contract design. Finally, we design a non-interactive zero-knowledge proof protocol to achieve correctness verification of off-chain key computation. Security analysis and simulation experiments demonstrate that our scheme achieves high security while significantly improving system performance. The time consumption for data users to obtain attribute private keys is controlled at tens of milliseconds.
{"title":"L2-MA-CPABE: A ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof","authors":"Zhixin Ren, Yimin Yu, Enhua Yan, Taowei Chen","doi":"10.1016/j.jksuci.2024.102247","DOIUrl":"10.1016/j.jksuci.2024.102247","url":null,"abstract":"<div><div>To enhance the security of ciphertext-policy attribute-based encryption (CP-ABE) and achieve fully distributed key generation (DKG), this paper proposes a ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof based on Layer-2 and multi-authority CP-ABE. Firstly, we enhance the system into two layers and construct a Layer-2 distributed key management service framework. This framework improves system efficiency and scalability while reducing costs. Secondly, we design the proof of trust contribution (PoTC) consensus algorithm to elect high-trust nodes responsible for DKG and implement an incentive mechanism for key computation through smart contract design. Finally, we design a non-interactive zero-knowledge proof protocol to achieve correctness verification of off-chain key computation. Security analysis and simulation experiments demonstrate that our scheme achieves high security while significantly improving system performance. The time consumption for data users to obtain attribute private keys is controlled at tens of milliseconds.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102247"},"PeriodicalIF":5.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705877","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}
Pub Date : 2024-11-17DOI: 10.1016/j.jksuci.2024.102245
Xingxu Fan , Xiaomei Yu , Xue Li , Fengru Ge , Yanjie Zhao
The rapid accumulation of large-scale electronic health records (EHRs) has witnessed the prosperity of intelligent medicine, such as medication recommendation (MR). However, most studies either fail to fully capture the structural correlation and temporal dependence among various medical records, or disregard the computational efficiency of the MR models. To fill this gap, we put forward a Lightweight Medication recommendation method which integrates bidirectional gate recurrent units (BiGRUs) with light graph convolutional networks (LGCNs) based on the multiple Graph Augmentation networks (LMGA). Specifically, BiGRUs are deployed to encode longitudinal visit histories and generate patient representations from a holistic perspective. Additionally, a memory network is constructed to extract local personalized features in the patients’ historical EHRs, and LGCNs are deployed to learn both drug co-occurrence and antagonistic relationships for integral drug representations with reduced computational resource requirements. Moreover, a drug molecular graph is leveraged to capture structural information and control potential DDIs in predicted medication combinations. Incorporating the representations of patients and medications, a lightweight and safe medication recommendation is available to promote prediction performance with reduced computational resource consumption. Finally, we conduct a series of experiments to evaluate the proposed LMGA on two publicly available datasets, and the experimental results demonstrate the superior performance of LMGA in MR tasks compared with the state-of-the-art baseline models.
{"title":"LMGA: Lightweight multi-graph augmentation networks for safe medication recommendation","authors":"Xingxu Fan , Xiaomei Yu , Xue Li , Fengru Ge , Yanjie Zhao","doi":"10.1016/j.jksuci.2024.102245","DOIUrl":"10.1016/j.jksuci.2024.102245","url":null,"abstract":"<div><div>The rapid accumulation of large-scale electronic health records (EHRs) has witnessed the prosperity of intelligent medicine, such as medication recommendation (MR). However, most studies either fail to fully capture the structural correlation and temporal dependence among various medical records, or disregard the computational efficiency of the MR models. To fill this gap, we put forward a <strong>L</strong>ightweight <strong>M</strong>edication recommendation method which integrates bidirectional gate recurrent units (BiGRUs) with light graph convolutional networks (LGCNs) based on the multiple <strong>G</strong>raph <strong>A</strong>ugmentation networks (LMGA). Specifically, BiGRUs are deployed to encode longitudinal visit histories and generate patient representations from a holistic perspective. Additionally, a memory network is constructed to extract local personalized features in the patients’ historical EHRs, and LGCNs are deployed to learn both drug co-occurrence and antagonistic relationships for integral drug representations with reduced computational resource requirements. Moreover, a drug molecular graph is leveraged to capture structural information and control potential DDIs in predicted medication combinations. Incorporating the representations of patients and medications, a lightweight and safe medication recommendation is available to promote prediction performance with reduced computational resource consumption. Finally, we conduct a series of experiments to evaluate the proposed LMGA on two publicly available datasets, and the experimental results demonstrate the superior performance of LMGA in MR tasks compared with the state-of-the-art baseline models.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102245"},"PeriodicalIF":5.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705872","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}
Pub Date : 2024-11-16DOI: 10.1016/j.jksuci.2024.102246
Weijie Wang , Wenhui Chen , Qinhon Lei , Zhe Li , Huihuang Zhao
The volume of time series data across various fields is steadily increasing. However, this unprocessed massive data challenges transmission efficiency, computational arithmetic, and storage capacity. Therefore, the compression of time series data is essential for improving transmission, computation, and storage. Currently, improving time series floating-point coding rules is the primary method for enhancing compression algorithms efficiency and ratio. This paper presents an efficient lossless compression algorithm for time series floating point data, designed based on existing compression algorithms. We employ three optimization strategies data preprocessing, coding category expansion, and feature refinement representation to enhance the compression ratio and efficiency of compressing time-series floating-point numbers. Through experimental comparisons and validations, we demonstrate that our algorithm outperforms Chimp, Chimp128, Gorilla, and other compression algorithms across multiple datasets. The experimental results on 30 datasets show that our algorithm improves the compression ratio of time series algorithms by an average of 12.25% and compression and decompression efficiencies by an average of 27.21%. Notably, it achieves a 24.06% compression ratio improvement on the IOT1 dataset and a 42.96% compression and decompression efficiency improvement on the IOT4 dataset.
{"title":"ACTF: An efficient lossless compression algorithm for time series floating point data","authors":"Weijie Wang , Wenhui Chen , Qinhon Lei , Zhe Li , Huihuang Zhao","doi":"10.1016/j.jksuci.2024.102246","DOIUrl":"10.1016/j.jksuci.2024.102246","url":null,"abstract":"<div><div>The volume of time series data across various fields is steadily increasing. However, this unprocessed massive data challenges transmission efficiency, computational arithmetic, and storage capacity. Therefore, the compression of time series data is essential for improving transmission, computation, and storage. Currently, improving time series floating-point coding rules is the primary method for enhancing compression algorithms efficiency and ratio. This paper presents an efficient lossless compression algorithm for time series floating point data, designed based on existing compression algorithms. We employ three optimization strategies data preprocessing, coding category expansion, and feature refinement representation to enhance the compression ratio and efficiency of compressing time-series floating-point numbers. Through experimental comparisons and validations, we demonstrate that our algorithm outperforms Chimp, Chimp<sub>128</sub>, Gorilla, and other compression algorithms across multiple datasets. The experimental results on 30 datasets show that our algorithm improves the compression ratio of time series algorithms by an average of 12.25% and compression and decompression efficiencies by an average of 27.21%. Notably, it achieves a 24.06% compression ratio improvement on the IOT1 dataset and a 42.96% compression and decompression efficiency improvement on the IOT4 dataset.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102246"},"PeriodicalIF":5.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705934","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}
Pub Date : 2024-11-16DOI: 10.1016/j.jksuci.2024.102237
Minglan Fu, Zhijie Zhang, ZouXi Wang, Debao Chen
Software crowdsourcing has become a cornerstone of the Internet economy because of its unique capacity for selecting optimal workers to complete specific tasks. However, new workers face limited task opportunities compared to experienced workers, which negatively impacts their motivation and decreases overall activity on crowdsourcing platforms. This reduced activity can harm platform reputation. To encourage the active participation of new workers, this study introduces a novel method to identify and match worker–task preferences. Our approach categorizes preferred tasks based on golden tasks, historical data, and worker interests. We then present the Multi-Objective Worker–Task Recommendation (MOWTR) algorithm, built upon the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The MOWTR algorithm allocates tasks by considering worker preferences, salaries, and capabilities, aiming to optimize collective team performance while minimizing team costs, especially for new workers. New crossover and two-stage mutation operators are incorporated to increase algorithm efficiency. Experimental evaluations on four real and synthetic datasets demonstrate that MOWTR outperforms four advanced baseline methods, confirming its effectiveness.
{"title":"The multi-objective task assignment scheme for software crowdsourcing platforms involving new workers","authors":"Minglan Fu, Zhijie Zhang, ZouXi Wang, Debao Chen","doi":"10.1016/j.jksuci.2024.102237","DOIUrl":"10.1016/j.jksuci.2024.102237","url":null,"abstract":"<div><div>Software crowdsourcing has become a cornerstone of the Internet economy because of its unique capacity for selecting optimal workers to complete specific tasks. However, new workers face limited task opportunities compared to experienced workers, which negatively impacts their motivation and decreases overall activity on crowdsourcing platforms. This reduced activity can harm platform reputation. To encourage the active participation of new workers, this study introduces a novel method to identify and match worker–task preferences. Our approach categorizes preferred tasks based on golden tasks, historical data, and worker interests. We then present the Multi-Objective Worker–Task Recommendation (MOWTR) algorithm, built upon the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The MOWTR algorithm allocates tasks by considering worker preferences, salaries, and capabilities, aiming to optimize collective team performance while minimizing team costs, especially for new workers. New crossover and two-stage mutation operators are incorporated to increase algorithm efficiency. Experimental evaluations on four real and synthetic datasets demonstrate that MOWTR outperforms four advanced baseline methods, confirming its effectiveness.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102237"},"PeriodicalIF":5.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705874","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号