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

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 integrity, clarity, novelty, and significance, based on peer review criteria from 11 top-tier CS conferences. We formulate the problem as two classification tasks: Accepted/Disputed/Rejected (ADR) and Accepted/Rejected (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.

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 $0.5\times 10^4$ bits to $5\times 10^4$ bits is 52.72 dB. Additionally, there are no errors in retrieving the carrier image and secret data.

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.

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.

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.

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.

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.

Previous studies have specifically demonstrated that incorporating high-quality side-information can notably enhance the steganographic security of JPEG images. To obtain more precise side-information estimates, researchers utilize sophisticated deblocking or denoising filters, which enhance security but require a substantial amount of time. The challenge lies in achieving a significant improvement in security and efficiency. This paper introduces a steganographic network named WTSNet, which leverages wavelet transform for estimating side-information through iterative optimization in both the pixel and wavelet domains. The wavelet domain is purposely designed to capture intricate high-frequency texture details and facilitate precise estimation of the side-information. Furthermore, the embedding cost is adjusted based on the polarity of the estimated rounding error, ensuring both secure and efficient performance. Experimental results demonstrate that the proposed scheme substantially enhances the security of conventional JPEG steganography, which relies on additive distortion. Moreover, it outperforms the state-of-the-art JPEG steganography methods based on estimating side-information while consuming less time.

Strain optimization aims to overproduce valuable metabolites by leveraging an understanding of biological systems, including metabolic networks and gene regulatory networks (GRNs). Accordingly, researchers proposed integrating metabolic networks and GRNs to be analyzed simultaneously. The proposed algorithms from 2002 to 2021 were rFBA, SR-FBA, iFBA, PROM, PROM2.0, TIGER, BeReTa, CoRegFlux, IDREAM, TRFBA, OptRAM, TRIMER, and PRIME. Each algorithm has different characteristics. Thus, using the appropriate algorithm for designing strain optimization is essential. Therefore, a critical review was conducted by synthesizing and analyzing the existing algorithms. Five aspects are discussed in this review: the strategic approaches, model of GRNs, source of GRNs, optimization, supplementary methods, and the programming language used. Based on the review, several algorithms were better at modeling integrated regulatory-metabolic networks with high confidence, i.e., PROM, PROM2.0, and TRFBA. A simulation was applied to six strains. The results show that PROM2.0 best predicted the production rate and time complexity. However, the model is heavily influenced by the quality and quantity of the gene expression data. In addition, there are inconsistencies between GRNs and the gene expression data. Thus, this review also discussed future work based on GRNs and gene expression data.