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

{"title":"Enhanced enterprise-student matching with meta-path based graph neural network","authors":"Fu Li ,&nbsp;Guangsheng Ma ,&nbsp;Feier Chen ,&nbsp;Qiuyun Lyu ,&nbsp;Zhen Wang ,&nbsp;Jian Zhang","doi":"10.1016/j.jksuci.2024.102116","DOIUrl":"10.1016/j.jksuci.2024.102116","url":null,"abstract":"<div><p>Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between <em>students</em>, <em>enterprises</em> and <em>industries</em>. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002052/pdfft?md5=b92e9095dd2f3d188041171d9ee66fb2&pid=1-s2.0-S1319157824002052-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639173","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":"Attribute-based linear homomorphic signature scheme based on key policy for mobile crowdsensing","authors":"Yuan Tian ,&nbsp;Tanping Zhou ,&nbsp;Xuan Zhou,&nbsp;Weidong Zhong,&nbsp;Xiaoyuan Yang","doi":"10.1016/j.jksuci.2024.102114","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102114","url":null,"abstract":"<div><p>Compared with traditional wireless sensor networks, mobile crowdsensing networks have advantages of low cost, easy maintenance, and high scalability, which will play a role in city-level data sensing scenarios in the future. So far, linear homomorphic signatures based on Public Key Instruction,identity, as well as certificateless, have been proposed in wireless sensor networks to resist the data contamination. However, these signature schemes cannot perform finer-grained signature verification, and these signature schemes do not realize the separation of users’ sensitive information from their data. To solve the above problems, we design an attribute-based linear homomorphic signature scheme for large-scale wireless network built with mobile smart devices. First, we give the definition of the attribute-based linear homomorphic signature scheme based on key policy (KP-ABLHS). Second, we construct KP-ABLHS by incorporating attribute-based signature and linear homomorphic coding signature scheme. Finally, we prove our protocol is secure in random oracle model (ROM) and use Python pairing-based cryptography library (pypbc) to implement the scheme. The experimental results show that our scheme is as efficient as Li et al.’s scheme and has the advantage of signing the set of attributes, while the efficiency of our scheme is significantly better than that of scheme Boneh et al.’s scheme.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002039/pdfft?md5=5422bf34152eb0c9ba54efd3a750f137&pid=1-s2.0-S1319157824002039-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540368","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":"Content-based quality evaluation of scientific papers using coarse feature and knowledge entity network","authors":"Zhongyi Wang ,&nbsp;Haoxuan Zhang ,&nbsp;Haihua Chen ,&nbsp;Yunhe Feng ,&nbsp;Junhua Ding","doi":"10.1016/j.jksuci.2024.102119","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102119","url":null,"abstract":"<div><p>Pre-evaluating scientific paper quality aids in alleviating peer review pressure and fostering scientific advancement. Although prior studies have identified numerous quality-related features, their effectiveness and representativeness of paper content remain to be comprehensively investigated. Addressing this issue, we propose a content-based interpretable method for pre-evaluating the quality of scientific papers. Firstly, we define quality attributes of computer science (CS) papers as <em>integrity</em>, <em>clarity</em>, <em>novelty</em>, and <em>significance</em>, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: <em>Accepted/Disputed/Rejected</em> (ADR) and <em>Accepted/Rejected</em> (AR). Subsequently, we construct fine-grained features from metadata and knowledge entity networks, including text structure, readability, references, citations, semantic novelty, and network structure. We empirically evaluate our method using the ICLR paper dataset, achieving optimal performance with the Random Forest model, yielding F1 scores of 0.715 and 0.762 for the two tasks, respectively. Through feature analysis and case studies employing SHAP interpretable methods, we demonstrate that the proposed features enhance the performance of machine learning models in scientific paper quality evaluation, offering interpretable evidence for model decisions.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002088/pdfft?md5=8465c32ce03880aedb7757f4009be933&pid=1-s2.0-S1319157824002088-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141607709","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":"Optimized reversible data hiding technique based on multidirectional prediction error histogram and fluctuation-based adaptation","authors":"Dima Kasasbeh,&nbsp;Mohammed Anbar","doi":"10.1016/j.jksuci.2024.102112","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102112","url":null,"abstract":"<div><p>Reversible Data Hiding Techniques (RDH) play an increasingly pivotal role in the field of cybersecurity. Overlooking the properties of the carrier image and neglecting the influence of texture can lead to undesirable distortions and irreversible data hiding. In this paper, a novel block-based RDH technique is proposed that harnesses the relative correlation between multidirectional prediction error histograms (MPEH) and pixel fluctuation values to mitigate undesirable distortions and enable RDH, thereby ensuring heightened security and efficiency in the distribution process and improving the robustness of the block-based RDH technique. The proposed technique uses a combination of pixel fluctuation and local complexity measures to determine the best embedding locations within smooth regions based on the cumulative peak regions of the MPEH with the lowest fluctuation values. Similarly, during the extraction process, the same optimal embedding locations are identified within smooth regions. The multidirectional prediction error histograms are then used to accurately extract the hidden data from the pixels with lower fluctuation values. Overall, the experimental results highlight the effectiveness and superiority of the proposed technique in various aspects of data embedding and extraction, and demonstrate that the proposed technique outperforms other state-of-the-art RDH techniques in terms of embedding capacity, image quality, and robustness against attacks. The average Peak Signal-to-Noise Ratio (PSNR) achieved with an embedding capacity ranging from <span><math><mrow><mn>0</mn><mo>.</mo><mn>5</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span> bits to <span><math><mrow><mn>5</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span> bits is 52.72 dB. Additionally, there are no errors in retrieving the carrier image and secret data.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002015/pdfft?md5=3c4ab46b7edb28a126a4dde971428787&pid=1-s2.0-S1319157824002015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540365","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":"Enhancing autonomous driving through intelligent navigation: A comprehensive improvement approach","authors":"Zihao Xu ,&nbsp;Yinghao Meng ,&nbsp;Zhen Yin ,&nbsp;Bowen Liu ,&nbsp;Youzhi Zhang ,&nbsp;Mengmeng Lin","doi":"10.1016/j.jksuci.2024.102108","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102108","url":null,"abstract":"<div><p>In this paper, an intelligent navigation system is developed to achieve accurate and rapid response to autonomous driving. The system is improved with three modules: target detection, distance measurement, and navigation obstacle avoidance. In the target detection module, the YOLOv7x-CM model is proposed to improve the efficiency and accuracy of target detection by introducing the CBAM attention mechanism and MPDioU loss function. In the obstacle distance measurement module, the concept of an off-center angle is introduced to optimize the traditional monocular distance measurement method. In the obstacle avoidance module, acceleration jump and steering speed constraints are introduced into the local path planning algorithm TEB, and the TEB-S algorithm is proposed. Finally, this paper evaluates the performance of the system modules using the KITTI dataset and the BDD100K dataset. It is demonstrated that YOLOv7x-CM improves the mAP @ 0.5 metrics by 5.3% and 6.8% on the KITTI dataset and the BDD100K dataset, respectively, and the FPS also increases by 35.4%. Secondly, for the optimized monocular detection method, the average relative distance error is reduced by 9 times. In addition, the proposed TEB-S algorithm has a shorter obstacle avoidance path and higher efficiency than the normal TEB algorithm.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001976/pdfft?md5=3501a1d07cb3bae41fa97be3fa787122&pid=1-s2.0-S1319157824001976-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540367","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 empirical study on the state-of-the-art methods for requirement-to-code traceability link recovery","authors":"Bangchao Wang ,&nbsp;Zhiyuan Zou ,&nbsp;Hongyan Wan ,&nbsp;Yuanbang Li ,&nbsp;Yang Deng ,&nbsp;Xingfu Li","doi":"10.1016/j.jksuci.2024.102118","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102118","url":null,"abstract":"<div><p>Requirements-to-code traceability link recovery (RC-TLR) can establish connections between requirements and target code artifacts, which is critical for the maintenance and evolution of large software systems. However, to the best of our knowledge, there is no existing experimental study focused on state-of-the-art (SOTA) methods for the RC-TLR problem, and there is also a lack of uniform benchmarks for evaluating new methods in the field. We developed a framework to identify SOTA methods using the Systematic Literature Review method and applied it to research in the RC-TLR field from 2018 to 2023. Through experiments replication on 13 datasets using 6 methods, we observed that for information retrieval-based methods, Close Relations between Target artifacts-based method (CRT), TraceAbility Recovery by Consensual biTerms (TAROT), and Fine-grained TLR (FTLR) performed well on COEST dataset, while Combining Part-Of-Speech with information-retrieval techniques (Conpos) and TAROT achieve promising results in large datasets. As concerns machine learning-based methods, Random Forest consistently exhibits strong performances on all datasets. We hope that this study can provide a comparative benchmark for performance evaluation in the RC-TLR field. The resource repository that we have established is expected to alleviate the workload of researchers in performance analysis, and promote progress of the field.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002076/pdfft?md5=8ae41f972f5fcb180b95390116c548b9&pid=1-s2.0-S1319157824002076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141595548","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":"Corrigendum to “Social media sentiment analysis and opinion mining in public security: Taxonomy, trend analysis, issues and future directions” [J. King Saud Univ. – Comput. Inform. Sci. 35(9) (2023) 101776]","authors":"Mohd Suhairi Md Suhaimin ,&nbsp;Mohd Hanafi Ahmad Hijazi ,&nbsp;Ervin Gubin Moung ,&nbsp;Puteri Nor Ellyza Nohuddin ,&nbsp;Stephanie Chua ,&nbsp;Frans Coenen","doi":"10.1016/j.jksuci.2024.102121","DOIUrl":"10.1016/j.jksuci.2024.102121","url":null,"abstract":"","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002106/pdfft?md5=58e9bbc651755fea7d7a677d6344355b&pid=1-s2.0-S1319157824002106-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141696587","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":"Scalability and diversity of StarGANv2-VC in Arabic emotional voice conversion: Overcoming data limitations and enhancing performance","authors":"Ali H. Meftah ,&nbsp;Yousef A. Alotaibi ,&nbsp;Sid Ahmed Selouani","doi":"10.1016/j.jksuci.2024.102091","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102091","url":null,"abstract":"<div><p>Emotional Voice Conversion (EVC) for under-resourced languages like Arabic faces challenges due to limited emotional speech data. This study explored strategies to mitigate dataset scarcity and improve Arabic EVC performance. Fundamental experiments (Speaker-Dependent, Gender-Dependent, Gender-Independent) were conducted using the KSUEmotions dataset to analyze speaker, gender, and model impacts. Data augmentation techniques like time stretching and phase shuffling artificially increased data diversity. Attention mechanisms integrated into StarGANv2-VC aimed to better capture emotional cues. Transfer learning leveraged the larger English Emotional Speech Database (ESD) to enhance the Arabic system. A novel “Reordering Speaker-Emotion Data” approach treated each emotion as a separate speaker to expand the emotional variability. Our comprehensive approach, combining transfer learning, data augmentation, and architectural modifications, demonstrates the potential to overcome dataset limitations and enhance the performance of Arabic EVC systems.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001800/pdfft?md5=6225b12a8e2dd7caf92c9cebc9d43dd5&pid=1-s2.0-S1319157824001800-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479892","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":"Pairing-free Proxy Re-Encryption scheme with Equality Test for data security of IoT","authors":"Gang Han ,&nbsp;Le Li ,&nbsp;Baodong Qin ,&nbsp;Dong Zheng","doi":"10.1016/j.jksuci.2024.102105","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102105","url":null,"abstract":"<div><p>The construction of IoT cloud platform brings great convenience to the storage of massive IoT node data. To ensure security of data collected by the IoT nodes, the data must be encrypted when the nodes upload it. However, it arises some challenge problems, such as efficient retrieval on encrypted data, secure de-duplication and massive data recovery. Proxy Re-Encryption with Equality Test (PREET) can be used to solve these problems. But, existing PREET schemes are based on bilinear pairings, which have low operational efficiency. In order to improve the overall operational efficiency, this paper constructs a Pairing-Free PREET scheme (PF-PREET). Security of this scheme is guaranteed by the Gap Computational Diffie–Hellman assumption in random oracle model. It is demonstrated that the PF-PREET scheme can achieve ciphertext indistinguishability for malicious users and one-wayness for malicious servers. It still has the same security level compared to existing PREET schemes by expanding the scope of the equality test. We discussed the efficiency of the proposed PF-PREET scheme in terms of overall running time, average running time of each phase and time overhead of tag test. The simulated experiment shows a certain improvement compared to PREET schemes using bilinear pairing.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001940/pdfft?md5=b5a75e08f9f6702a67314403d83dd189&pid=1-s2.0-S1319157824001940-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479317","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":"Rumor gatekeepers: Unsupervised ranking of Arabic twitter authorities for information verification","authors":"Hend Aldahmash ,&nbsp;Abdulrahman Alothaim ,&nbsp;Abdulrahman Mirza","doi":"10.1016/j.jksuci.2024.102111","DOIUrl":"https://doi.org/10.1016/j.jksuci.2024.102111","url":null,"abstract":"<div><p>The advent of online social networks (OSNs) has catalyzed the formation of novel learning communities. Identifying experts within OSNs has become a critical component for facilitating knowledge exchange and enhancing self-awareness, particularly in contexts such as rumor verification processes. Research efforts aimed at locating authorities in OSNs are scant, largely due to the scarcity of annotated datasets. This work represents a contribution to the domain of unsupervised learning to address the challenge of authorities’ identification in Twitter. We have employed advanced natural language processing technique to transfer knowledge concerning topics in the Arabic language and to discern the semantic connections among candidates within Twitter in zero-shot learning. We take advantage of the Single-labeled Arabic News Articles Dataset (SANAD) to perform the process of extracting domain features and applying these features in finding authorities using the Authority Finding in Arabic Twitter (AuFIN) dataset. Our evaluation assessed the extent of extracted topical features transferred and the efficacy of authorities’ retrieval in comparison to the latest unsupervised models in this domain. Our approach successfully extracted and integrated the limited available topical semantic features of the language into the representation of candidates. The findings indicate that our hybrid model surpasses those that rely solely on lexical features of language and network topology, as well as other contemporary approaches to topic-specific expert finding.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824002003/pdfft?md5=497e343dafa3a0f464edc892c03b7186&pid=1-s2.0-S1319157824002003-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542406","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}