Concurrency and Computation-Practice & Experience最新文献

{"title":"Design and Evaluation of Energy-Efficient Whisper Dot-Product Kernel Offloading on a CGLA Architecture","authors":"Takuto Ando,&nbsp;Yu Eto,&nbsp;Ayumu Takeuchi,&nbsp;Yasuhiko Nakashima","doi":"10.1002/cpe.70826","DOIUrl":"https://doi.org/10.1002/cpe.70826","url":null,"abstract":"<div>\u0000 \u0000 <p>In this paper, we implement and evaluate Whisper dot-product kernel offloading on IMAX, a programmable coarse-grained linear arrays (CGLAs) architecture. Whisper-tiny.en profiling on an ARM Cortex-A72 shows that dot-product operations account for 90.6% of FP16 execution time and 87.1% of Q8_0 execution time. To address this kernel bottleneck, we combine kernel mapping, local-memory sizing, and burst scheduling. The implementation uses inline FP16-to-FP32 conversion, 2-way SIMD FMA on a 64-bit datapath, column-wise multithreading, and mixed execution in which aligned vector segments run on IMAX and residual segments run concurrently on the host CPU. We evaluate the design with an FPGA prototype and a 28 nm ASIC projection at 840 MHz. For Whisper-tiny.en, 32 KB local memory and burst length 16 jointly minimize PDP and EDP. Under a TDP-based cross-platform comparison, the projected IMAX records a PDP of 11.58 J for Whisper-tiny.en Q8_0, 2.35<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> lower than Jetson AGX Orin (27.16 J) and 10.48<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ times $$</annotation>\u0000 </semantics></math> lower than RTX 4090 (121.38 J). The same design extends to Whisper-base.en and Whisper-small.en, where the PDP gap narrows as 32 KB local-memory coverage drops from 93.8% for tiny to about 66.5% for base and small. These results position IMAX as a programmable architecture for lower-PDP local ASR in the tiny-model regime.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 13","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"FSDMeL: Few-Shot Enabled Distributed Meta-Learning Model for Image Recognition","authors":"Ganesh Vitthal Kadam,&nbsp;Ghanshyam Prasad Dubey,&nbsp;Pramod P. Jadhav","doi":"10.1002/cpe.70806","DOIUrl":"https://doi.org/10.1002/cpe.70806","url":null,"abstract":"<div>\u0000 \u0000 <p>Nowadays, image recognition has become a hot topic due to the widespread applicability in numerous applications, including healthcare, military applications, and infrared warnings. Numerous machine learning, deep learning, and transformer-based image recognition techniques are designed to perform object detection. Conversely, their inability to obtain fine-grained feature representation, increased computational complexity, and overfitting issues lower the speed and accuracy of the image recognition process. Hence, to eradicate certain downsides and to incur accurate recognition of the images, a Few-shot enabled distributed meta-learning (FSDMeL) framework is proposed. The proposed FSDMeL model combines a few-shot meta-learning strategy that primarily enables the model to learn new tasks with limited data and transfer knowledge between related classes to improve recognition performances. In line with this, the FSDMeL model also integrates various sophisticated mechanisms for feature extraction, which leverage diverse as well as relevant information to avoid overfitting issues. Moreover, the FSDMeL model also includes several benefits such as faster training times, improved generalization to unseen tasks, and the ability to provide correct detection without increasing training complexity. According to the assessment, the proposed model gains an accuracy of 98.04%, precision of 98.77%, and recall of 97.67% using the Fashion MNIST dataset compared to existing methods for image recognition.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 13","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Empirical Comparison of Kryo Serialization Optimization Strategies in Apache Spark: Cloud-Based Evaluation at Scale","authors":"Lennah Etyang,&nbsp;Lawrence Nderu,&nbsp;Waweru Mwangi","doi":"10.1002/cpe.70808","DOIUrl":"https://doi.org/10.1002/cpe.70808","url":null,"abstract":"<div>\u0000 \u0000 <p>Serialization is a major performance bottleneck in Apache Spark, accounting for up to 40% of total execution time in data processing workloads. Although Kryo serialization presents a remarkable improvement over Java's native serialization, ensuring optimum configuration is a challenging task due to complexity on parameter interactions and workload-dependent performance behavior. In this research, we conducted a comprehensive comparative study of four Kryo optimization strategies deployed on production-scale AWS EMR infrastructure. We carried out an evaluation using the TPC-DS benchmark at a 100GB scale across the following workload types: (1) Java serialization baseline, (2) default Kryo configuration, (3) rule-based optimization based on best practices, and (4) a new adaptive framework that dynamically changes parameters based on workload characteristics. To our knowledge, this is the first systematic comparative evaluation of Kryo optimization strategies that integrates workload-aware adaptation with cost-performance analysis, distinguishing our work from black-box automatic tuning frameworks such as SparkTune, AutoSpark, and DeepTune. Our experimental results indicate the adaptive framework performs 38.4% faster execution than Java serialization (<i>p</i> &lt; 0.001, Cohen's <i>d</i> = 1.42, 95% CI [1.11, 1.73]) as well as a 26.8% advantage over default Kryo (<i>p</i> = 0.0021). Ablation studies reveal that buffer sizing contributes 34.2% of the total improvement, compression selection 29.3%, class registration 17.1%, and adaptivity itself 23.9%, together explaining 65.1% of performance variance (<i>R</i>² = 0.651). The study shows extensive workload dependence on optimization effectiveness as well, with join-heavy queries experiencing a 52.3% gain but scan-intensive operations showing only an 18.7% gain. Economic studies find that the performance gains are directly linked to cloud cost reductions with the total spending in our model reduced by 38.7% as compared to baseline configurations, though these savings accrue only when clusters are elastically scaled. A cost-equivalent baseline demonstrates that achieving the same performance through resource scaling alone would require 62.5% more worker nodes, highlighting optimization value beyond simple horizontal scaling. These results question the one-size-fits-all optimization models and provide evidence-based configuration recommendations to practitioners that consider workload variability and cloud infrastructure behavior validated on held-out TPC-DS queries with 90.5% recommendation accuracy.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 13","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"An Effective Cognitive Cybersecurity System of Cognitive Sciences Using Adaptive Transformer-Based Deep Learning Model","authors":"P. Shyamala Bharathi,&nbsp;T. Jayaprakash Reddy","doi":"10.1002/cpe.70796","DOIUrl":"https://doi.org/10.1002/cpe.70796","url":null,"abstract":"<div>\u0000 \u0000 <p>Based on the cybersecurity perspectives, cognitive sciences are useful to improve human factor capabilities, especially in complex systems developed by technologies like social networks, Internet of Things (IoT), mobile, and cloud that generate vast amounts of data. In recent days, it is more important for scientists to analyze how cognitive sciences are helpful in improving human capabilities for cybersecurity tasks. The current cognitive security models aim to improve cybersecurity tasks by integrating some solutions. Nevertheless, they still have problems related to the intrinsic shortcomings of each component. Hence, by utilizing cognitive science, a novel cognitive cybersecurity system is developed in this work. The required data are garnered initially from diverse online sources. Then, the gathered data is fed as input to the Adaptive Transformer-based Variational Autoencoder with Long Short-Term Memory (AT-VAELSTM) for decision-making purposes. Here, the decision-making process is improved by optimizing the AT-VAELSTM model parameters with the assistance of the Renovated Position-based Pine Cone Optimization Algorithm (RP-PCOA). Finally, an efficient outcome is achieved from this model, and the necessary experimental analysis is performed for the designed model by comparing it with the baseline approaches to guarantee the supremacy of the model.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Computational Framework for Adaptive Circuit Selection in Noisy Quantum Communication Simulations","authors":"Kaushik Dehingia,&nbsp;Nimisha Dutta","doi":"10.1002/cpe.70803","DOIUrl":"https://doi.org/10.1002/cpe.70803","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Quantum communications and quantum key distribution (QKD) occupy a unique position at the intersection of quantum information processing, stochastic noise and distributed network control. Theoretical guarantees for the security of QKD based on quantum mechanics have been developed over the last several years. In order to effectively implement QKD, however, we must understand that practical implementations are limited by factors such as decoherence, imperfection of devices, and non-homogeneous, varying environmental conditions of our distributed networks. These challenges require the need to develop adaptive computational frameworks that will dynamically adjust to changing levels of noise and to resource availability in order to successfully operate QKD, rather than relying on fixed configurations of protocols. The current study presents a means of adaptively selecting circuits for quantum communication simulated in a noisy environment using a high level of reproducibility in terms of computational frameworks and focuses upon workloads similar to QKD. Quantum communication can be treated as a decentralized, stochastic decision-making process in which an adapting controller selects between a set of available quantum circuits based on observable network characteristics. Using the decoherence-free subspace (DFS) encoding method and an entanglement-based protocol that incorporates purification, the use of modular circuit templates has allowed for the selection of templates guided by the use of learned decision policies as opposed to a fixed protocol being predetermined. Unlike previous works applying reinforcement learning to tune fixed protocol parameters or measurement strategies Yang et al. and Xu et al., we model decoherence-mitigation primitives—DFS and entanglement purification—as &lt;i&gt;dynamically selectable circuit templates&lt;/i&gt;. In this sense, the agent acts on protocol structure instead of tuning parameters, to allow compositional adapting, on heterogeneous and adversarial network conditions. (This amounts to a qualitatively different and under-explored control abstraction compared to the literature). Adaptive control utilizes a quantum reinforcement learning (QRL) based six qubit, four layer parameterized quantum circuit with 48 trainable parameters and a deep Q-network (DQN) classical baseline. The agents notice the same three-dimensional representation of state with channel loss, node-specific noise and eavesdropping indicators. The framework is compared with more than 50 independent trials on a simulated 10-node quantum network under a physically enriched noise model with depolarizing noise, collective dephasing, thermal relaxation (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;70&lt;/mn&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 ","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"RAAS: Runtime Adaptive Approximation System","authors":"Lucas Reis,&nbsp;Sandro Rigo,&nbsp;Lucas Wanner","doi":"10.1002/cpe.70797","DOIUrl":"https://doi.org/10.1002/cpe.70797","url":null,"abstract":"<p>Software-level approximation techniques, such as loop perforation and functional replacement, can improve energy and performance in error-tolerant applications, but typically require extensive programmer intervention even when compiler support is available. Design-time and compile-time approximation approaches also make static decisions, limiting the ability to adapt approximation levels dynamically in response to runtime conditions or inputs. We present RAAS, a Runtime Adaptive Approximation System that identifies and applies approximation opportunities dynamically within a Just-In-Time compiler. The programmer marks approximation regions using simple annotations, and provides either built-in or custom quality metrics along with acceptable error bounds. RAAS explores the approximation space at runtime, evaluating candidate transformations for performance and accuracy, and converges to configurations that maximize speedup while respecting the allowed error threshold. Evaluation on application kernels from domains such as simulation and image processing shows speedups of up to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>4</mn>\u0000 <mo>.</mo>\u0000 <mn>6</mn>\u0000 <mo>×</mo>\u0000 </mrow>\u0000 <annotation>$$ 4.6times $$</annotation>\u0000 </semantics></math> with quality degradation below 10%, outperforming static compilation approaches even when accounting for runtime adaptation overhead.</p>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.70797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Obstacle Avoidance Path Planning for Spherical Joint Actuator Based on Improved APF-Bi-RRT* Algorithm","authors":"Xiwen Guo,&nbsp;Bo Han,&nbsp;Qunjing Wang,&nbsp;Guoli Li,&nbsp;Zhibo Liu,&nbsp;Ao Tan","doi":"10.1002/cpe.70805","DOIUrl":"https://doi.org/10.1002/cpe.70805","url":null,"abstract":"<div>\u0000 \u0000 <p>To avoid collisions between an end tool driven by a novel spherical joint actuator and surrounding obstacles during operations in static scenarios such as assembly and spraying, which may lead to system malfunctions, this paper proposes a method that integrates an improved artificial potential field (APF) and a dynamic target biasing strategy with the bidirectional rapidly-exploring random tree star (Bi-RRT*) algorithm. This approach, termed the Improved APF-Bi-RRT* algorithm, is combined with a novel collision detection method to achieve efficient path planning. Firstly, collision detection is reframed from analyzing contact between the end tool and obstacles to directly defining the unreachable region of the end point. On this basis, an environment map is reconstructed by leveraging the spherical motion characteristics of the end point, thereby satisfying workspace constraints while reducing the complexity of subsequent obstacle avoidance algorithms. Then, the Bi-RRT* algorithm is applied for path search on this map, and the improved APF and dynamic target biasing strategy are integrated into the search process to minimize redundant nodes and accelerate convergence. Finally, simulation and experimental results demonstrate that the proposed method not only ensures reliable obstacle avoidance for the end tool driven by the spherical joint actuator in static known obstacle environments, but also reduces planning time and path length compared with the RRT*, P-RRT*, Bi-RRT*, and APF-Bi-RRT* algorithms.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"DWT-DCSIFT: A Novel Feature Representation for Facial Kinship Verification","authors":"Fatiha Mokdad,&nbsp;Bahia Yahya-Zoubir,&nbsp;Meriem Fedila","doi":"10.1002/cpe.70807","DOIUrl":"https://doi.org/10.1002/cpe.70807","url":null,"abstract":"<div>\u0000 \u0000 <p>Facial kinship verification is a significant area of research that aims to determine whether two people are biologically related based on their facial similarity. This study proposes a new feature representation for facial kinship verification, named Discrete Wavelet Transform-Dense Color Scale Invariant Feature Transform (DWT-DCSIFT), which combines the frequency, color, and texture descriptions of the face image into a single representation. The frequency information is provided by the 2D-DWT, allowing spatial and detail features to be captured at different resolutions. The color and texture information is provided by the DCSIFT descriptor, which uses a dense color histogram to exploit the color information of the face image, and a dense SIFT algorithm to exploit the facial texture features. The combined DWT-DCSIFT feature vector incorporates the spectral information representing the shape features, the color information, and the texture details of the face image, leading to a richer representation for kinship verification. The Fisher score algorithm selects the top-ranked features, which are then subjected to dimensionality reduction using principal component analysis. The resulting data is then fed into the support vector machines classifier for the final decision. The proposed kinship verification approach produced competitive results and demonstrated enhanced performance over state-of-the-art techniques. In particular, the proposed model achieved high accuracy in discriminating between kin and non-kin relationships, reaching 81.60% for the KinFaceW-I dataset and an impressive 91.05% for the KinFaceW-II dataset.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Secure and Efficient Ring Learning With Error Based Three-Party Authenticated Key Agreement Scheme for Wireless Mobile Communication Systems","authors":"Sunil Kumar,&nbsp;Arvind Yadav","doi":"10.1002/cpe.70755","DOIUrl":"https://doi.org/10.1002/cpe.70755","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;Quantum computing poses a mounting and concrete threat to the cryptographi protocols underpinning mobile communication systems, where both security and computational efficiency are critical constraints. Existing three-party authenticated key agreement (3PAKA) schemes either rely on classically hard problems broken by Shor's algorithm or, among postquantum proposals, fail to simultaneously achieve user anonymity, unlinkability, and perfect forward secrecy at acceptable cost for resource-constrained mobile devices. We first conduct a rigorous cryptanalysis of the recent lattice-based scheme of Singh et al. and uncover four concrete vulnerabilities: an &lt;i&gt;identity recovery vulnerability and scalability failure&lt;/i&gt; arising from XOR masking with a static server key, which forces an &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;O&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ mathcal{O}(N) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; exhaustive identity search and opens a denial-of-service vector; &lt;i&gt;blind acceptance by the responder&lt;/i&gt;, as User &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;B&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ B $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; forwards the initiator's message without verifying the sender's identity; &lt;i&gt;authentication blindness at the initiator&lt;/i&gt;, since the response received by User &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;A&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ A $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; carries no cryptographic proof of User &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;B&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ B $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;'s identity; and &lt;i&gt;server-side incomplete verification&lt;/i&gt; caused by a missing session parameter &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;B&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {C}_B $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, which the server requires but never receives. To remedy these deficiencies, we propose a Three-Party Post-Quantum Mutual Authentication and Key Agreement (MAKA) scheme for mobile devices, built on the Ideal-Lattice Ring Learning With Errors (Ring-LWE) assumption with parameters &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;1024&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ n=1024 $$&lt;/annotation&gt;\u0000 &lt;/semantic","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"StaQ: Unified Forbidden Sequence Removal and Lossless Compression for Reliable Data Preservation","authors":"Jiashu Wu,&nbsp;Yibo Wang,&nbsp;Yang Wang","doi":"10.1002/cpe.70754","DOIUrl":"https://doi.org/10.1002/cpe.70754","url":null,"abstract":"<div>\u0000 \u0000 <p>Forbidden sequences are nucleotide patterns that cause errors in DNA synthesis and sequencing, compromising data integrity. Addressing them is essential for reliable DNA storage. Existing methods struggle with efficient removal while preserving original data. To solve this, this paper proposes a stack-queue-based algorithm, called <i>StaQ</i>, for efficient removal of forbidden sequences in DNA data storage. StaQ employs a two-phase process: (1) a removal phase uses a stack to detect and remove forbidden sequences based on a predefined taboo set, logging positions in a queue; (2) a restoration phase reconstructs the original sequence by reversing the stack and reinserting forbidden sequences using queue records, ensuring lossless recovery. Tested on seven NCBI DNA sequences, StaQ achieves an average BPB of 1.693, outperforming existing methods and enhancing DNA storage reliability. The algorithm maintains linear time and space complexity, making it scalable for large-scale genomic data. Furthermore, StaQ's integrated removal and restoration strategy enables simultaneous forbidden-sequence filtering and data compression, improving both biosafety and storage efficiency.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"38 12","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148209011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}