Computer Standards & Interfaces最新文献

{"title":"DEA-GAO: A two-stage approach optimal controller placement in software-defined networks using data envelopment analysis","authors":"Fatemeh Dashti,&nbsp;Ali Ghaffari,&nbsp;Nahideh Derakhshanfard,&nbsp;Shiva Taghipoureivazi","doi":"10.1016/j.csi.2025.104109","DOIUrl":"10.1016/j.csi.2025.104109","url":null,"abstract":"<div><div>Software-defined networks (SDN) have demonstrated considerable benefits in various practical domains by decoupling the control plane from the data plane, thus facilitating programmable network management. This paper presents a two-stage approach for solving the problem of controller placement called DEA-GAO. In the first stage, this strategy assumes the SDN network as a graph and using Data Envelopment Analysis (DEA) and relying on graph centrality metrics such as closeness centrality, betweenness centrality, and eigenvector centrality, calculates the efficiency of nodes to determine the optimal locations for deploying controllers. In the second stage, to allocate switches to controllers, the proposed strategy employs the Green Anaconda Optimization algorithm (GAO) to achieve an optimal allocation while considering network parameters such as average delay, load balancing, and reliability. Finally, to assess the efficacy of the proposed methodology, it is juxtaposed with three extant methods utilizing diverse datasets from the Internet Topology Zoo. The experimental findings indicate that the proposed approach significantly surpasses the existing methods, specifically the hybrid RDMCP-PSO algorithm, heuristic CPP algorithm and PSO algorithm in terms of both average delay (8.8 %, 28.8 % and 22.2 % respectively) and controller utilization (1.5 %, 7.3 % and 32 % respectively).</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104109"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust zero-watermarking method for multi-medical images based on Chebyshev–Fourier moments and Contourlet-FFT","authors":"Xinhui Lu ,&nbsp;Guangyun Yang ,&nbsp;Yu Lu ,&nbsp;Xiangguang Xiong","doi":"10.1016/j.csi.2025.104115","DOIUrl":"10.1016/j.csi.2025.104115","url":null,"abstract":"<div><div>Classical robust watermarking methods embed secret data into a cover image designed to protect its copyright. However, they suffer from the problem of balancing imperceptibility and robustness. To address this issue, the impact of conventional attacks on the stability of feature vectors extracted from the cover image is examined. Accordingly, we proposed a zero-watermarking method with high attack resistance for multi-medical images by employing Contourlet transform (CT), Chebyshev–Fourier moments (CHFMs), and fast Fourier transform (FFT). First, each medical image is normalized separately, and the normalized images are fused using a dual-tree complex wavelet transform-based method. Second, the effective region is extracted and subjected to the CT. The CHFMs of the low-frequency sub-bands are calculated, and the FFT is performed on the generated amplitude sequence to construct a feature matrix. A feature image is generated by combining the magnitude of each feature value with the overall mean. Finally, the copyrighted image is encrypted using the Lorenz chaotic system and Fibonacci Q-matrix, after which an exclusive-OR operation is applied between the generated feature image and the encrypted copyrighted image to produce a zero-watermarking signal. The results show that the proposed method exhibits excellent resistance to attack with a normalized correlation coefficient of up to 0.994 between the extracted image and the original copyrighted one. Furthermore, the average anti-attack performance of our proposed method is approximately 2% higher compared to similar existing methods, indicating that our proposed method is highly resistant to conventional, geometric, and combinatorial attacks.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104115"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing hashed timelock cross-chain protocol with trusted middleman in blockchain networks","authors":"Wenhua Huang,&nbsp;Yuwei Deng,&nbsp;Jingyu Feng,&nbsp;Gang Han,&nbsp;Wenbo Zhang","doi":"10.1016/j.csi.2026.104129","DOIUrl":"10.1016/j.csi.2026.104129","url":null,"abstract":"<div><div>Cross-chain interoperability has emerged as a pivotal factor in enabling seamless data interaction and value circulation across diverse blockchain networks. Nevertheless, current cross-chain technologies necessitate advancements to satisfy the escalating need for efficient bidirectional data exchange. Addressing this, our work focuses on refining cross-chain protocols, with a core emphasis on elevating transaction efficiency, reliability, and security. Our innovation centers around a strengthened hashed timelock cross-chain protocol grounded in trusted middlemen. To safeguard the security and confidentiality of middlemen engaged in cross-chain transactions, we introduce an ingenious anonymous identity authentication mechanism. This mechanism empowers middlemen to execute auxiliary cross-chain transactions while concealing their actual identities. Additionally, we propose a behavior-based assessment of middlemen, utilizing distinct indicators to gauge their trustworthiness in each cross-chain transaction. We introduce both current and historical trust values, providing insights into middlemen's real-time reliability and long-term stability. This approach effectively thwarts attempts by malicious middlemen to manipulate trust values, mitigating security vulnerabilities in cross-chain transactions. Furthermore, by clearing redundant blocks, we not only decrease storage consumption but also facilitate the storage of a substantial amount of identity data and trust data of middlemen. Rigorous security analysis demonstrates our scheme's alignment with foundational security requirements and resilience against common attacks. Furthermore, our simulation results underscore the potency of our trust evaluation scheme, substantiating its efficacy in ensuring middlemen credibility and detecting malicious actors.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104129"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient and secure multi-user kNN queries with dynamic POIs updating","authors":"Yining Jia ,&nbsp;Yali Liu ,&nbsp;Congai Zeng ,&nbsp;Xujie Ding ,&nbsp;Jianting Ning","doi":"10.1016/j.csi.2025.104112","DOIUrl":"10.1016/j.csi.2025.104112","url":null,"abstract":"<div><div>The <span><math><mi>k</mi></math></span>-nearest neighbors (<span><math><mi>k</mi></math></span>NN) query is a key operation in spatial and multimedia databases, which is widely applied in fields such as electronic healthcare and Location-Based Services (LBS). With the rapid development of cloud computing, uploading private data of Data Owner (DO) to Cloud Servers (CS) has become a trend. However, existing <span><math><mi>k</mi></math></span>NN queries schemes are not designed for multi-user environments, cannot timely update the points of interest (POIs) stored in CS, and suffer from low query efficiency. Therefore, this paper proposes efficient and secure multi-user <span><math><mi>k</mi></math></span>NN queries with dynamic POIs updating, named DESM<span><math><mi>k</mi></math></span>NN, which achieves secure multi-user <span><math><mi>k</mi></math></span>NN queries. To improve query efficiency, DESM<span><math><mi>k</mi></math></span>NN adopts a two-stage search framework, which consists of an initial filtering stage based on hierarchical clustering to effectively constrain the search range, followed by a more efficient precise search stage. Based on this framework, DESM<span><math><mi>k</mi></math></span>NN designs a set of security protocols for efficient query processing and enables dynamic POIs updates. Meanwhile, DESM<span><math><mi>k</mi></math></span>NN not only utilizes Distributed Two Trapdoors Public-Key Cryptosystem (DT-PKC) to enable multi-user queries but also ensures data privacy, query privacy, result privacy and access pattern privacy. Moreover, DESM<span><math><mi>k</mi></math></span>NN can verify the correctness and completeness of queries results. Finally, security analysis proves that DESM<span><math><mi>k</mi></math></span>NN meets the formal security definition of multiparty computation, and experimental evaluation shows that DESM<span><math><mi>k</mi></math></span>NN improves query efficiency by up to 45.5% compared with existing <span><math><mi>k</mi></math></span>NN queries scheme.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104112"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining decision boundaries via dynamic label adversarial training for robust traffic classification","authors":"Haoyu Tong ,&nbsp;Meixia Miao ,&nbsp;Yundong Liu ,&nbsp;Xiaoyu Zhang ,&nbsp;Xiangyang Luo ,&nbsp;Willy Susilo","doi":"10.1016/j.csi.2025.104111","DOIUrl":"10.1016/j.csi.2025.104111","url":null,"abstract":"<div><div>Network traffic classification plays a critical role in securing modern communication systems, as it enables the identification of malicious or abnormal patterns within traffic data. With the growing complexity of network environments, deep learning models have emerged as a compelling solution due to their ability to automatically learn discriminative representations from raw traffic. However, these models are highly vulnerable to adversarial examples, which can significantly degrade their performance by introducing imperceptible perturbations. While adversarial training (AT) has emerged as a primary defense, it often suffers from label noise, particularly when hard labels are forcibly assigned to adversarial examples whose true class may be ambiguous. In this work, we first analyze the detrimental effect of label noise on adversarial training, revealing that forcing hard labels onto adversarial examples can cause excessive shifts of the decision boundary away from the adversarial examples, which in turn degrades the model’s generalization. Motivated by the theoretical analysis, we propose Dynamic Label Adversarial Training (DLAT), a novel AT framework that mitigates label noise via dynamically mixed soft labels. DLAT interpolates the logits of clean and adversarial examples to estimate the labels of boundary-adjacent examples, which are then used as soft labels for adversarial examples. By adaptively aligning the decision boundary toward the vicinity of adversarial examples, the framework constrains unnecessary boundary shifts and alleviates generalization degradation caused by label noise. Extensive evaluations on network traffic classification benchmarks validate the effectiveness of DLAT in outperforming standard adversarial training and its variants in both robustness and generalization.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104111"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy consumption assessment in embedded AI: Metrological improvements of benchmarks for edge devices","authors":"Andrea Apicella ,&nbsp;Pasquale Arpaia ,&nbsp;Luigi Capobianco ,&nbsp;Francesco Caputo ,&nbsp;Antonella Cioffi ,&nbsp;Antonio Esposito ,&nbsp;Francesco Isgrò ,&nbsp;Rosanna Manzo ,&nbsp;Nicola Moccaldi ,&nbsp;Danilo Pau ,&nbsp;Ettore Toscano","doi":"10.1016/j.csi.2025.104120","DOIUrl":"10.1016/j.csi.2025.104120","url":null,"abstract":"<div><div>This manuscript proposes a new method to improve the MLCommons protocol for measuring power consumption on Microcontroller Units (MCUs) when running edge Artificial Intelligence (AI). In particular, the proposed approach (i) selectively measures the power consumption attributable to the inferences (namely, the predictions performed by Artificial Neural Networks — ANN), preventing the impact of other operations, (ii) accurately identifies the time window for acquiring the sample of the current thanks to the simultaneous measurement of power consumption and inference duration, and (iii) precisely synchronize the measurement windows and the inferences. The method is validated on three use cases: (i) Rockchip RV1106, a neural MCU that implements ANN via hardware neural processing unit through a dedicated accelerator, (ii) STM32 H7, and (iii) STM32 U5, high-performance and ultra-low-power general-purpose microcontroller, respectively. The proposed method returns higher power consumption for the two devices with respect to the MLCommons approach. This result is compatible with an improvement of selectivity and accuracy. Furthermore, the method reduces measurement uncertainty on the Rockchip RV1106 and STM32 boards by factors of 6 and 12, respectively.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104120"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publicly verifiable covert two-party computation with punitive blame","authors":"Chuan Zhao ,&nbsp;Jun Xu ,&nbsp;Shengnan Zhao ,&nbsp;Song Han ,&nbsp;Chunpeng Ge ,&nbsp;Qiuliang Xu","doi":"10.1016/j.csi.2026.104132","DOIUrl":"10.1016/j.csi.2026.104132","url":null,"abstract":"<div><div>Publicly verifiable covert (PVC) security model allows honest parties in secure multi-party computation (MPC) to generate a public proof that can be verified by an external party to check misbehavior. PVC first takes into account the deterrent effects of publicly damaging the reputation of dishonest parties. However, the dishonest party may still choose to cheat if the potential gains outweigh the minor reputational consequences. Moreover, using proxies like shell companies can insulate the actual party from direct reputational harm in case of detected cheating. Therefore, a more realistic deterrent in PVC is required to improve its practicality. Inspired by the financial restriction mechanism (Faust et al. PKC 2022), we propose a PVC security model incorporating punitive blame to enhance the deterrent effects of the dishonest party. Our security model achieves strong defamation resistance by allowing participants to carry out a series of algorithms, including Blame, PreJudge, Appeal, and Judge. Furthermore, based on a new compatible extension technique for Signed-OT, we propose a secure two-party protocol in the new PVC model. The experiment shows approximate improvements in computational and communication costs of <span><math><mrow><mn>1</mn><mo>.</mo><mn>5</mn><mo>−</mo><mn>2</mn><mo>.</mo><mn>6</mn><mo>×</mo></mrow></math></span> and <span><math><mrow><mn>2</mn><mo>.</mo><mn>3</mn><mo>−</mo><mn>2</mn><mo>.</mo><mn>9</mn><mo>×</mo></mrow></math></span> over the PVC protocol, respectively.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104132"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ARMOR: A multi-layered adaptive defense framework for robust deep learning systems against evolving adversarial threats","authors":"Mahmoud Mohamed,&nbsp;Fayaz AlJuaid","doi":"10.1016/j.csi.2025.104117","DOIUrl":"10.1016/j.csi.2025.104117","url":null,"abstract":"<div><h3>Introduction:</h3><div>Adversarial attacks represent a major challenge to deep learning models deployed in critical fields such as healthcare diagnostics and financial fraud detection. This paper addresses the limitations of single-strategy defenses by introducing ARMOR (Adaptive Resilient Multi-layer Orchestrated Response), a novel multi-layered architecture that seamlessly integrates multiple defense mechanisms.</div></div><div><h3>Methodology:</h3><div>We evaluate ARMOR against seven state-of-the-art defense methods through extensive experiments across multiple datasets and five attack methodologies. Our approach combines adversarial detection, input transformation, model hardening, and adaptive response layers that operate with intentional dependencies and feedback mechanisms.</div></div><div><h3>Results:</h3><div>Quantitative results demonstrate that ARMOR significantly outperforms individual defense methods, achieving a 91.7% attack mitigation rate (18.3% improvement over ensemble averaging), 87.5% clean accuracy preservation (8.9% improvement over adversarial training alone), and 76.4% robustness against adaptive attacks (23.2% increase over the strongest baseline).</div></div><div><h3>Discussion:</h3><div>The modular framework design enables flexibility against emerging threats while requiring only 1.42<span><math><mo>×</mo></math></span> computational overhead compared to unprotected models, making it suitable for resource-constrained environments. Our findings demonstrate that activating and integrating complementary defense mechanisms represents a significant advance in adversarial resilience.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"97 ","pages":"Article 104117"},"PeriodicalIF":3.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LHSC-SGC: A lightweight hybrid signcryption scheme for smart grid communications in heterogeneous cryptographic public-key systems","authors":"Ting-Chieh Ho,&nbsp;Yuh-Min Tseng,&nbsp;Sen-Shan Huang","doi":"10.1016/j.csi.2025.104078","DOIUrl":"10.1016/j.csi.2025.104078","url":null,"abstract":"<div><div>For smart grid communications (SGC), ensuring the transmission security of power consumption data is crucial to prevent unauthorized access and achieve the undeniability (unforgeability) of the data. In this context, signcryption is particularly important for SGC because it combines signature and encryption in a scheme to provide both unforgeability and confidentiality for data transmission while reducing computational complexity. Nowadays, based on different cryptographic public-key systems (CPKSs), many signcryption schemes have been proposed, but they are applied to a single CPKS. When an original CPKS is upgraded to another new CPKS, all associated cryptographic mechanisms must be rewritten to ensure that all users (i.e., original, upgraded and new users) can operate all functionalities normally in such heterogeneous CPKSs. However, the existing signcryption schemes lack the heterogeneously compatible property that makes them unsuitable for heterogeneous CPKSs. In this paper, we propose the <em>first</em> lightweight hybrid signcryption scheme for SGC in heterogeneous CPKSs, named as the LHSC-SGC scheme. Security theorems are formally proven that the LHSC-SGC scheme possesses both unforgeability and confidentiality for data transmission. When compared to the existing lightweight signcryption schemes, the LHSC-SGC scheme has better performance and possesses heterogeneously compatible property. By performance simulation experiences, the LHSC-SGC scheme is well-suitable for SGC with IoT devices (i.e., smart meters).</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"96 ","pages":"Article 104078"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey study on meta-heuristic-based feature selection approaches of intrusion detection systems in distributed networks","authors":"Yashar Pourardebil khah ,&nbsp;Mirsaeid Hosseini Shirvani ,&nbsp;Javid Taheri","doi":"10.1016/j.csi.2025.104074","DOIUrl":"10.1016/j.csi.2025.104074","url":null,"abstract":"<div><div>With the emergence of IoT and expanding the coverage of distributed networks such as cloud and fog, security attacks and breaches are becoming distributed and expanded too. Cybersecurity attacks can disrupt business continuity or expose critical data, leading to significant failures. The Intrusion Detection Systems (IDSs) as a remedy in such networks play a critical role in this ecosystem to find an attack at the earliest time and the countermeasure is performed if necessary. Artificial intelligence techniques such as machine learning-based and meta-heuristic-based approaches are being pervasively applied to prepare smarter IDS components from logged network traffic. The network traffic is recorded in the form of data sets for further analysis to detect traffic behavior from past treatments. Feature selection is a prominent approach in creating the prediction model to recognize feature network connection is normal or not. Since the feature selection problem in large datasets is NP-Hard and utilizing only heuristic-based approaches is not as efficient as desired, meta-heuristic-based approaches attract research attention to prepare highly accurate prediction models. To address the issue, this paper presents a subjective classification of published literature. Then, this presents a survey study on meta-heuristic-based feature selection approaches in preparing efficient IDSs. It investigates several kinds of literature from different angles and compares them in terms of used metrics in the literature to give broad insights into readers for advantages, challenges, and limitations. It can pave the way by highlighting research gaps for further processing and improvement in the future by interested researchers in the field.</div></div>","PeriodicalId":50635,"journal":{"name":"Computer Standards & Interfaces","volume":"96 ","pages":"Article 104074"},"PeriodicalIF":3.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}