Array最新文献

{"title":"Ladderpath: An efficient algorithm for revealing nested hierarchy in sequences","authors":"Jingwen Zhang ,&nbsp;Xiao Xie ,&nbsp;Xiaodong Deng ,&nbsp;Jing Wang ,&nbsp;Xiaojun Hu ,&nbsp;Yiping Wang ,&nbsp;Hu Zhu ,&nbsp;Fengyao Zhai ,&nbsp;Yu Liu","doi":"10.1016/j.array.2025.100663","DOIUrl":"10.1016/j.array.2025.100663","url":null,"abstract":"<div><div>Ladderpath, rooted in Algorithmic Information Theory (AIT), uncovers nested and hierarchical structures in symbolic sequences through minimal compositional reconstruction. It approximates Kolmogorov complexity by identifying reusable subsequences that enable efficient reconstruction of complex sequences. The proposed algorithm improves upon earlier implementations by introducing key optimizations in substring enumeration and reuse filtering, allowing it to scale to sequence systems with tens or even hundreds of millions of characters. Ladderpath produces a standardized JSON format that encodes compositional dependencies and hierarchies, and supports a variety of downstream tasks, including compression, shared motif extraction, cross-sequence similarity analysis, and structural visualization. Its domain-agnostic design enables broad applicability across areas such as genomics, natural language, symbolic computation, and program analysis. Beyond providing a practical approximation of complexity, Ladderpath also offers structural insight into the modular grammar of sequences, pointing to a deeper connection between algorithmic complexity and compositional hierarchies observed in real-world data.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100663"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart grid privacy data encryption and sharing algorithm based on multi-key homomorphic encryption","authors":"Xuehai Chen ,&nbsp;Yantong Lin ,&nbsp;Zhimin Liang ,&nbsp;Zhenmin He","doi":"10.1016/j.array.2025.100655","DOIUrl":"10.1016/j.array.2025.100655","url":null,"abstract":"<div><div>To realize the effective planning and regulation of smart grids, it is necessary to ensure the smart grid private data sharing's security. A smart grid privacy data encryption and sharing algorithm based on multi-key homomorphic encryption is proposed. This algorithm is based on the smart meters' private data collected at the device layer, and encrypts the data by using the multi-key homomorphic encryption method of the key generation center. The computing layer interacts with smart meters within its coverage area through fog nodes. After the data collected from smart meters are authenticated and aggregated, the data is transmitted to the cloud storage layer for storage. The data stored in the cloud storage layer is encrypted by using multi - key homomorphic encryption methods and transmitted to the server. After decryption, the server can obtain the details of the private data of each subarea and realize the encryption and sharing of the privacy data of the smart grid. The test results show that the algorithm has good encryption performance, with encryption times all within 700 ms. The data decryption probability is above 99.22 %, and the communication overhead required for shared transmission is above 2000bit in all cases. The intrusion rate is within 0.3 %, ensuring the safe sharing of private data.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100655"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing security in IoT networks: A multifaceted approach to vulnerability analysis and protection","authors":"Zohre Arabi ,&nbsp;Ramin Rajabi Oskouei ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.array.2025.100626","DOIUrl":"10.1016/j.array.2025.100626","url":null,"abstract":"<div><div>The rapid proliferation of the Internet of Things (IoT) has transformed modern technology by bridging the physical and digital realms. Yet, the explosive growth of connected devices—expected to surpass 50 billion by 2025—has introduced substantial security concerns. This study investigates critical vulnerabilities within IoT systems, particularly at the device and network levels, focusing on risks such as data breaches, unauthorized access, and distributed denial-of-service (DDoS) attacks. It explores the significance of implementing standardized security practices for interoperable internet-connected hardware within various environments. Despite the simplicity and feasibility of adopting such standards, many manufacturers neglect essential security protocols, leaving devices exposed. Much like pre-flight checklists in aviation, foundational security principles should be embedded into hardware design; however, innovation in this area has been largely overlooked.</div><div>We present an innovative two-phase methodology aimed at strengthening IoT security. Manufacturers often prioritize rapid deployment over protection, resulting in devices that are ill-equipped to handle sophisticated cyber threats. Conventional security approaches, based on static and generic rules, are ill-suited to the diverse, resource-constrained, and protocol-heavy IoT landscape. Our second phase involves detecting device vulnerabilities using advanced tools, such as Nmap for network probing and Binwalk for firmware analysis. Key protective measures—including secure boot processes, firmware hashing, and secure integrated circuits (ICs)—are employed to safeguard sensitive data and ensure firmware integrity. Experimental results validate the approach's effectiveness in identifying and mitigating vulnerabilities. Visual data, including port distribution charts and CVSS-based risk assessments, highlight the necessity of prioritizing high-impact threats. Although there are limitations, such as difficulties in updating legacy devices and analyzing large networks, the proposed framework significantly reduces cybersecurity risks, builds trust in IoT systems, and establishes a solid foundation for future security developments.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100626"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gaze-adaptive neural pre-correction for mitigating spatially varying optical aberrations in near-eye displays","authors":"Yi Jiang,&nbsp;Ye Bi,&nbsp;Yinng Li,&nbsp;Pengfei Li,&nbsp;Shengnan Qin,&nbsp;Zichao Shu,&nbsp;Chengrui Le","doi":"10.1016/j.array.2025.100654","DOIUrl":"10.1016/j.array.2025.100654","url":null,"abstract":"<div><div>Near-eye display (NED) technology constitutes a fundamental component of head-mounted display (HMD) systems. The compact form factor required by HMDs imposes stringent constraints on optical design, often resulting in pronounced wavefront aberrations that significantly degrade visual fidelity. In addition, natural eye movements dynamically induce varying blur that further compromises image quality. To mitigate these challenges, a gaze-contingent neural network framework has been developed to compensate for aberrations within the foveal region. The network is trained in an end-to-end manner to minimize the discrepancy between the optically degraded system output and the corresponding ground truth image. A forward imaging model is employed, in which the network output is convolved with a spatially varying point spread function (PSF) to accurately simulate the degradation introduced by the optical system. To accommodate dynamic changes in gaze direction, a foveated attention-guided module is incorporated to adaptively modulate the pre-correction process, enabling localized compensation centered on the fovea. Additionally, an end-to-end trainable architecture has been designed to integrate gaze-informed blur priors. Both simulation and experimental validations confirm that the proposed method substantially reduces gaze-dependent aberrations and enhances retinal image clarity within the foveal region, while maintaining high computational efficiency. The presented framework offers a practical and scalable solution for improving visual performance in aberration-sensitive NED systems.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100654"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven comparative analysis of Agile and Waterfall methodologies: Predicting cost and schedule variances using statistical and machine learning approaches","authors":"Utkarsh Mishra,&nbsp;Narayanan Ganesh","doi":"10.1016/j.array.2025.100665","DOIUrl":"10.1016/j.array.2025.100665","url":null,"abstract":"<div><div>Project management methodologies like Agile, Waterfall, etc., play an impactful role in key performance indicators of the project, such as cost variance, schedule variance, etc. In this work, we deep dive into these variances with data-driven techniques and discover machine learning models for cost estimation. To demonstrate the efficacy of our approach, we processed a dataset with Agile and Waterfall project attributes which was collected by means of survey conducted online about 100 developers from various companies. We had through categorical encoding, statistical analysis, hypothesis testing, and predictive modeling to predict and compare the projects which can be successful. In the initial stages of Exploratory Data Analysis (EDA), it can be observed that the distribution of cost and schedule variance is not uniform across the waterfall and agile approaches, whereby the mean cost and schedule variances is 2.14 and SD is 1.32 for Agile projects and the mean cost and schedule variances for waterfall projects is higher at 3.87 with SD of 1.89. A T-test conducted to compare the methodologies results in a test statistic of −4.72 and a p-value of 0.00002, indicating a statistically significant difference in cost and schedule variances between Agile and Waterfall projects. Additionally, the use of project attributes to train a linear regression model for predicting cost variance and schedule variance for both waterfall and agile approaches achieves an average MAE of 0.98 and an average MSE of 1.54, indicating moderate predictive accuracy in the models. They emphasize that, on average, Agile projects have a lower cost and schedule variance than Waterfall projects and strengthen the impact of the project methodology on effort deviations. The study highlights the role of predictive analytics in project management and advocates the adoption of machine learning for more accurate cost estimation. The next step is to investigate more advanced modeling techniques and the use of additional project parameters to improve predictive performance and project planning.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100665"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tabular and graph-based representations for noise and missing data in robust machine learning","authors":"Golam Imran ,&nbsp;Md Parvez Hossain ,&nbsp;Mahmudul Hasan ,&nbsp;Md Tarek Hasan ,&nbsp;Ohidujjaman","doi":"10.1016/j.array.2026.100697","DOIUrl":"10.1016/j.array.2026.100697","url":null,"abstract":"<div><div>The performance of machine learning models in industrial settings is often limited by noise and missing values in real-world data. Tabular data representations, commonly used in traditional machine learning, may not effectively capture complex relationships or maintain reliability under such data degradation. This study comparatively evaluates the robustness of tabular and graph-based data representations for machine learning models when faced with data corruption. Using a real-world steel industry energy consumption dataset, we assess six models: Random Forest, XGBoost, Multi-Layer Perceptron (MLP), Graph Convolutional Network, SAGE, and Graph Attention Network, across clean, noisy, missing, and combined noise and missing data scenarios. A novel transformation technique converts tabular data into graph structures to facilitate relational learning in graph-based models. Graph-based models demonstrated 30.8% greater robustness than tabular models, as measured by their lower average drop in classification accuracy across missing, noisy, and combined data corruption scenarios. These findings pave the way for deploying more resilient artificial intelligence (AI) systems in complex industrial environments, emphasizing the critical role of relational data representations in robust machine learning. For validation, we applied another study with the UCI Machine Learning Repository: the Concrete Compressive Strength Dataset, and found comparable resonance in this regard.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100697"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of adeno-associated viruses with semi-supervised learning algorithm","authors":"Al Hossain ,&nbsp;Umme Hani Konok ,&nbsp;Raihan Ul Islam ,&nbsp;Mohammad Shahadat Hossain ,&nbsp;Prashanta Dutta","doi":"10.1016/j.array.2025.100648","DOIUrl":"10.1016/j.array.2025.100648","url":null,"abstract":"<div><div>Transfer learning–based deep supervised learning methods have recently become a mainstay for classification tasks. They have been applied to distinguish adeno-associated viruses (AAVs) carrying single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), or no DNA (Empty) from resistive pulse images generated by solid-state nanopore experiments. However, obtaining large quantities of labeled nanopore data, which play a crucial role in enhancing the accuracy of supervised learning, can be challenging. In this research work, we applied FixMatch—a simple yet powerful semi-supervised algorithm, which utilizes a smaller amount of labeled data to classify AAVs with ssDNA, dsDNA, or no DNA from resistive pulse sensor data. In FixMatch, the teacher model is developed by fine-tuning one of the pre-trained deep convolutional networks—GoogleNet, ResNet-50, WideResNet-50-2, and VGG-19. Using only 20% of the data as labeled and 80% as unlabeled, FixMatch achieved mean accuracies between 90% and 100% with low variance. Our results demonstrate that FixMatch can match or exceed the performance of supervised learning while dramatically reducing labeling effort. Furthermore, the prediction accuracy of FixMatch was much superior to that of MixMatch (a commonly used semi-supervised learning model). However, FixMatch's training time was notably longer compared to fully supervised approaches. Nevertheless, the high accuracy achieved with FixMatch, even with a small proportion of labeled data, opens new avenues for streamlining classification in data-scarce environments.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100648"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure access to implantable medical devices: A deep learning-driven protocol using ECG signals","authors":"Amirhossein Safari ,&nbsp;Mohsen Hooshmand ,&nbsp;Sadegh Sadeghi ,&nbsp;Peyman Phalevani ,&nbsp;Nasour Bagheri ,&nbsp;Mahmoud Jabbarpour","doi":"10.1016/j.array.2025.100650","DOIUrl":"10.1016/j.array.2025.100650","url":null,"abstract":"<div><div>The Internet of Things in medical applications demands strong privacy and security, particularly for implantable medical devices (IMDs) that must be reconfigured wirelessly through programmer devices. Existing IMD access-control protocols offer partial protection but still face challenges in accuracy and reliability. Biometric signals, especially Electrocardiogram (ECG), provide a promising alternative for secure and continuous authentication in such IoT systems. This work proposes four deep learning-based authorization approaches enabling secure communication between an IMD and a nearby programmer using ECG-derived representations. The methods fall into two categories: (1) Reliable IMD (RI) and unReliable IMD (uRI), where RI assumes the IMD’s internally recorded ECG is trustworthy (authentication uses only the programmer’s signal), while uRI requires cross-verification between IMD and programmer signals; and (2) Omni (multi-user authentication) versus Mono (single-user authentication). Mono-RI and Omni-RI employ convolutional neural network (CNN)-based architectures, whereas Mono-uRI and Omni-uRI use Siamese networks to support both authentication and replay attack detection. Experimental results show that all four models outperform state-of-the-art solutions. Mono-RI achieves 97.73% authentication accuracy, while Omni-uRI reaches 98.75%. Replay attack detection rates are 99.19% for Mono models and 90.73% for Omni models. These findings demonstrate that the proposed methods are effective across different operational scenarios, supporting both high-security modes and emergency modes that demand rapid access, depending on the desired balance between performance and computational complexity.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100650"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-information fusion based frequency-aligned network for few-shot segmentation","authors":"Xu Yu,&nbsp;Xinghua Wang,&nbsp;Shengning Lu,&nbsp;Zhihao Ren,&nbsp;Yong Liang","doi":"10.1016/j.array.2026.100703","DOIUrl":"10.1016/j.array.2026.100703","url":null,"abstract":"<div><div>Few-shot segmentation endeavors to extract knowledge from limited samples and then use it to segment images in novel categories. However, owing to the paucity of source data, extracted knowledge is often insufficient to handle variations in local features or issues such as perspective distortion. To address this problem, we propose MFANet, which effectively leverages textual and instance information to achieve accurate segmentation. Specifically, the network includes a frequency interaction module that integrates features across different frequency bands, alleviating discrepancies between frequency-specific features and helping identify defective ones. Subsequently, an information fusion module combines textual information with feature representations to mitigate challenges such as ambiguous local features or perspective distortions. Moreover, to ensure consistent learning between text and image modalities, we introduce a multimodal alignment module that maps both modalities into a unified embedding space. Extensive experiments on <span><math><mrow><mi>P</mi><mi>A</mi><mi>S</mi><mi>C</mi><mi>A</mi><mi>L</mi><mo>−</mo><msup><mrow><mn>5</mn></mrow><mrow><mi>i</mi></mrow></msup></mrow></math></span> and <span><math><mrow><mi>C</mi><mi>O</mi><mi>C</mi><mi>O</mi><mo>−</mo><mn>2</mn><msup><mrow><mn>0</mn></mrow><mrow><mi>i</mi></mrow></msup></mrow></math></span> demonstrate that MFANet achieves superior performance.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100703"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146184692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HXTS: An efficient tweakable block cipher solution for enhancing sector data security","authors":"Zhe Yu ,&nbsp;Dan Liu ,&nbsp;Kai Nie ,&nbsp;Zilong Zhao ,&nbsp;Fang Chen ,&nbsp;Shunmin Zhou ,&nbsp;Bin Wang","doi":"10.1016/j.array.2026.100712","DOIUrl":"10.1016/j.array.2026.100712","url":null,"abstract":"<div><div>Aiming at the problems of low encryption efficiency and weak security of HCTR and XTS, this paper designs an efficient tweakable block cipher solution—HXTS to enhance the security of sector data. HXTS is designed on a new structure of Hash-XTS-Hash, which reduces the computation amount of the hash function to improve the encryption efficiency through the fine-grained partitioning of sector data into UnitData, and enhances the security of ciphertexts by updating the tweak parameter of each UnitData with the plaintext, ciphertext or function. In this paper, we first prove the feasibility of HXTS through data encryption/decryption experiments. Then through diffusivity experiments, it proves that HXTS has excellent data protection capability. Finally, through performance comparison experiments, it is proved that HXTS outperforms XTS and HCTR when encrypting folders, and the encryption efficiency of HXTS is improved by 20% compared with XTS, and the growth rate of memory occupation is reduced by 51% compared with XTS. HXTS, as a new tweakable block cipher scheme design, can provide a reference for further optimization of sector encryption.</div></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"29 ","pages":"Article 100712"},"PeriodicalIF":4.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}