ABSTRACT The usage of multimedia data has grown exponentially over the past few decades, with a significant portion of its content in the form of images and videos, which are transmitted across various public networks. As the prevalence of video content continues to rise, so does the critical need for enhanced data security. Recently, several techniques have been proposed to create efficient data security procedures, focusing on security, robustness and computational complexity. This paper proposes a secure and robust video encryption technique based on the concepts of confusion and diffusion. During the confusion phase, horizontal and vertical permutations of pixel values are performed using a permutation box and then pixel values are substituted based on a lookup table. In the diffusion phase, the pixel values of the first frame are XORed with a defined box, and then, sequentially, the pixel values in the remaining frames are XORed using the previous frame as a reference. Each stage requires keys for processing that are generated by three different chaotic logistic maps, which successfully passed the NIST test. These numbers are then XORed to implement a multi‐chaotic logistic map effectively. Simulation metrics, including entropy, correlation, NPCR, UACI, and so forth demonstrate the strength of the proposed method. The result analysis also states that the proposed methodology performs well in terms of Quantitative Security Analysis Correlation Analysis 0.0017, Entropy Analysis 7.99, Peak Signal to Noise Ratio 7.898, Mean Square Error 10549.37, Structural Similarity Index (0.0075), Key Space Analysis (), Differential Attack Analysis (Number of Pixel Change Rate 99.61 and Unified Average Change Intensity 33.46), Robustness Analysis (Salt and Paper Noise (pass), Gaussian Noise (pass), Occlusion Attack (pass) and Execution Time Analysis θ ( n )).

ABSTRACT Unmanned aerial‐vehicle (UAV) networks have been ubiquitous and play an inevitable role in internet‐of‐drones. Their reliance on mesh networking and the MAVLink protocol makes them highly vulnerable to severe and potentially lethal cyberattacks. The classical public‐key encryption/decryption scheme employing integer‐factorization and primitive‐root‐type problems (such as the RSA system) has been commonly used for many years; however, its security is compromised by future advancements in quantum computing (through Shor's algorithm). In this work, we present a quantum‐secure authentication protocol for UAV‐based IoD applications, emphasizing user anonymity. A security analysis is presented which guarantees correctness, privacy, and robustness against numerous active and passive attacks in both the classical and quantum settings. Moreover, a real‐time testbed experiment of running time on Raspberry Pi 5 boards to evaluate the computational overhead of different cryptographic primitives illustrates its practicality. Finally, a performance comparison of filtering accuracy and resistance to unknown attacks with related existing schemes demonstrates its scalability and usefulness in real‐world applications.

ABSTRACT Cyclic executives (CEs) offer the advantage of ensuring complete determinism with minimal runtime overhead, often making them the preferred choice for safety‐critical real‐time systems. However, generating CEs for multicore processors while addressing task precedence and exclusion relations presents significant challenges. In this paper, unlike previous work, we tackle these challenges by proposing integer linear programming (ILP) models to generate optimal preemptive and non‐preemptive CEs, considering both partitioned and global work allocation schemes. Additionally, we introduce a local search‐based heuristic to efficiently produce approximate solutions. Our methods are evaluated on both synthetic and benchmark instances from the literature, encompassing thousands of tasks and complex inter‐task dependencies, and include a direct comparison with a state‐of‐the‐art approximation method. The experimental results highlight the effectiveness of the proposed approaches in generating optimal or near‐optimal CEs for large‐scale task sets.

ABSTRACT Task‐based programming interfaces introduce a paradigm in which computations are decomposed into fine‐grained units of work known as “tasks”. StarPU is a runtime system originally developed to support task‐based parallelism on on‐premise heterogeneous architectures by abstracting low‐level hardware details and efficiently managing resource scheduling. It enables developers to express applications as task graphs with explicit data dependencies, which are then dynamically scheduled across available processing units, such as CPUs and GPUs. In recent years, major cloud providers have begun offering virtual machines equipped with both CPUs and GPUs, allowing researchers to deploy and execute parallel workloads in virtual heterogeneous clusters. However, the performance and cost effectiveness of executing StarPU‐based applications in public cloud environments remain unclear, particularly due to variability in hardware configurations, network performance, ever‐changing pricing models, and computing performance due to virtualization and multi‐tenancy. In this paper, we evaluate the performance and cost‐efficiency of StarPU on Amazon Elastic Compute Cloud (EC2) using dense linear algebra kernels and N‐Body simulations as case studies. Our experiments consider different cluster configurations, including powerful and more expensive instances with four NVIDIA GPUs per node (which we refer to as “fat nodes”), and less powerful and lower‐cost instances with a single NVIDIA GPU per node (which we refer to as “thin nodes”). Our results show that arithmetic precision affects the performance–cost trade‐off for dense linear algebra applications, whereas N‐Body simulations consistently achieve better cost‐efficiency on thin‐node clusters. These findings underscore the challenges of optimizing HPC workloads for performance and cost in cloud environments.

ABSTRACT The increasing availability of GPU accelerated architectures for high‐performance computing presents new opportunities for scientific software but also challenges due to the complexity of porting legacy codes to accelerator platforms. Directive‐based programming models such as OpenACC offer a minimally intrusive pathway to exploit GPU acceleration without requiring extensive rewriting of existing codes. The current work presents a comprehensive performance and portability study of a LatticeBoltzmann Method solver (PyLB) originally written in Python, Mpi4Py, and Fortran for CPU architectures, which is ported to GPUs using OpenACC directives applied to the Fortran routines. The performance of the solver is evaluated on NVIDIA V100, A100, and H100 GPUs available on the Jean Zay supercomputer from Institute for Development and Resources in Intensive Scientific Computing (IDRIS) in France. Roofline analysis and extensive strong and weak scalability tests are conducted, showing that the GPU‐enabled version of PyLB scales efficiently across multiple GPUs. The solver achieves performance on the H100 GPU equivalent to thousands of CPU cores and shows strong energy and carbon efficiency advantages over traditional CPU‐based simulations. The implementation is validated using classical benchmarks, including the decaying Taylor‐Green vortex and the flow over a 3‐D sphere. The results confirm the physical accuracy of the GPU port while highlighting its computational and environmental advantages.

ABSTRACT Private set operations (PSO) address key challenges in cross‐organizational data sharing where multiple transport entities securely verify shared cargo manifests or delivery schedules without exposing sensitive information. Private set intersection (PSI) enables secure computation of common elements across private datasets, while its variant, PSI with cardinality (PSI‐CA), reveals only the intersection size. The PSO protocol based on Diffie‐Hellman (DH) key agreement has emerged as a prominent solution for communication‐sensitive applications. However, current research efforts mostly concentrate on two‐party implementations, which face inherent limitations in addressing the communication complexity when scaling to multiple participants. In this work, we firstly present E3PSI‐CA, a novel three‐party PSO protocol. This construction for the semi‐honest model leverages bilinear maps in DH key agreement and Bloom Filters (BF) for compact set representation, achieving remarkable efficiency. By integrating private information retrieval (PIR) techniques, the party in E3PSI‐CAcan efficiently compute the set intersection. To address the inherent accuracy limitations of the BF and extend security to the malicious model, we further propose an enhanced PSI scheme. This variant is built upon a “Ring” structure, within which we employ DH key agreement and OKVS data structure to achieve security against any single malicious party, while the OKVS simultaneously ensures perfect accuracy (zero false positives). We prove the security through the ideal/real simulation paradigm and conduct performance evaluations.

ABSTRACT Hypertension continues to be a primary cause of global death, necessitating early and accurate forecasting for effective treatments. The existing methods have drawbacks such as class imbalance, poor modeling of sequential and spatial connections, high computation costs, and lack of interpretability, even though Deep Learning (DL) models offer possible solutions. To tackle these difficulties, we present VahigoNet, a novel blending DL model that incorporates vanilla recurrent neural networks (VRNN) for capturing temporal correlations, Google network for extracting hierarchical spatial features, and highway networks (HighwayNet) for adaptive feature refinements. To achieve strong generalization, we utilize the synthetic minority oversampling technique (SMOTE) for data balance. VahigoNet substantially outperforms baseline models, showing enhancements of 9.39% in accuracy, 10.27% in precision, 8.63% in recall, 9.39% in F1‐score, and 3.10% in area under the curve‐receiver operating characteristic. A 10‐fold cross validation method is utilized to assess the model's generalizability, markedly reducing overfitting and improving robustness. A paired t ‐test is performed to evaluate statistical significance, demonstrating that the enhancements are substantial and clinically relevant. Additionally, explainable artificial intelligence (AI) methodologies, including local Interpretable model‐agnostic explanations (LIME) and SHapley Additive exPlanations, are incorporated to provide both local and global perspectives on feature contributions. These explainability strategies enhance transparency, making VahigoNet a more interpretable and clinically reliable model for hypertension prediction. The results demonstrate that VahigoNet is an exceptionally efficient and transparent method, achieving a balance between predictive capability and practical relevance in medical diagnostics.

ABSTRACT Sarcasm, a complex linguistic form combining humor and criticism, often involves conveying meanings opposite to literal meanings, posing significant challenges in sentiment analysis. This endeavor becomes complicated when it comes to resource‐poor Indian indigenous languages like Telugu, which require more adequate resources and exhibit intricate morphology. Detecting sarcasm in such contexts necessitates the creation of a well‐balanced and annotated corpus. To address this gap, the primary aim of the paper is to curate a Telugu corpus of 10,000 conversations, including 5000 sarcastic and 5000 non‐sarcastic instances, using a multi‐annotator strategy. Additionally, this paper proposes a novel feature fusion network, that is, “FF‐Net”, that integrates neural features with manually extracted handcrafted features to detect sarcasm in Telugu conversational texts. To test and validate the proposed work, extensive experimentations were done and the results demonstrate the significance of using handcrafted features for sarcasm detection, with the proposed model achieving 95.65% accuracy, outperforming existing baselines by 2.75% and surpassing state‐of‐the‐art models by 2.80%. These results underline the efficacy of fusing automated and manual feature extraction techniques, offering a robust approach to sarcasm detection with improved performance.

ABSTRACT In the Spark distributed framework, data communication problems (network transfer overhead, network IO bottlenecks) caused by data transfer across nodes/racks are a common cause of performance degradation due to the inconsistency between the task execution location and the data location. Additionally, in heterogeneous environments, Spark's task scheduling strategy cannot fully utilize the advantages of high‐performance nodes. To address the above issues, firstly, this paper proposes a cost‐aware task selection strategy. The strategy models the cost of tasks by considering the impact of data locality and heterogeneous factors on the efficiency of job execution. For scenarios where data locality needs to be reduced for scheduling tasks, the task scheduling problem is transformed into a minimum weighted bipartite graph matching problem, and a greedy matching algorithm is used to solve for the minimum processing cost option. For scenarios that maintain the current data localization level for scheduling tasks, select the task execution with the largest change in task processing cost due to data localization changes. Secondly, the problem is that Spark's delay scheduling algorithm causes resources in the cluster to be in an unnecessary waiting state and reduces cluster resource utilization. In this paper, we propose an adaptive adjustment strategy for delay waiting time based on benefit assessment. This policy improves the resource utilization of the cluster by evaluating the benefit of delay waiting of the scheduler and dynamically adjusting the delay time based on the result of the evaluation. Finally, we implement the proposed strategy in Spark 3.0.0 and evaluate its performance using some representative benchmarks. The experimental results show that, compared with other task scheduling algorithms, the strategy proposed in this paper can effectively improve the execution efficiency of jobs, reduce the execution time of jobs by 15.8%–31.9%, and at the same time reduce the network traffic and improve the CPU utilization.

ABSTRACT With the widespread application of cloud computing, large‐scale datasets in fields such as deep learning are increasingly stored in the cloud. Advanced techniques for data integrity verification are necessitated due to the frequent incremental updates of these datasets. In practice, existing Merkle tree‐based schemes face challenges, including high computational costs, low real‐time performance, and inefficient handling of incremental updates. To address these issues, a novel data structure named dynamic Merkle tree ladder (DMTL) was proposed in this paper, which enhanced Merkle trees by establishing ladder rungs for each dataset and incorporating a flexible dataset partition strategy. Based on the DMTL, we designed an integrity verification scheme that supported adaptive incremental updates of cloud datasets. Experimental results demonstrated that our scheme had outperformed mainstream schemes in dynamic operation efficiency, especially under workloads with intensive data insertions. Security analysis further showed that our scheme could defend against malicious behaviors effectively.