Concurrency and Computation-Practice & Experience最新文献

Hyperspectral image (HSI) classification demands high-performance processing and accuracy due to data dimensionality and computational complexity. The support vector machine (SVM) algorithm, which is renowned for its ability to manage high-dimensional data through kernel techniques for classification tasks effectively, has shown promising results in HSI analysis and applications. Composite kernels enhance SVM efficacy by integrating multiple kernels to capture diverse data characteristics. Existing SVM algorithms based on composite kernels are susceptible to high latency, computationally intensive operations, and poor scalability, which makes it challenging to deploy efficiently on resource-limited high-performance platforms like field-programmable gate arrays (FPGAs). The paper proposes a novel composite weighted-summation kernel (WSK) that combines polynomial and hardware friendly kernels (HFKs). The WSK kernel has been integrated with SVM, resulting in a classifier named WSK-SVM. Furthermore, a scalable FPGA-based system on chip (SoC) architecture is developed for HSI classification using WSK-SVM. The architecture implements a fast kernel approximation method to compute a HFK which eliminates multiplications. The architecture leverages spatial and temporal parallelism, efficiently aggregating processing into clusters of parallel binary WSK-SVM classifiers using the one-vs-one (OvO) methodology. The approach is scalable and achieves a level of spatial parallelism that facilitates the concurrent classification of multiple pixels. Two different SoC boards were used to implement the design. One board was based on the Xilinx Zynq-7000 and featured four parallel classifiers, and the second board was based on the Zynq UltraScale+ MPSoC and featured eleven parallel classifiers. High classification accuracy (>∖,99%) was achieved on benchmark datasets (Indian Pines, Pavia Centre, and Pavia University) using 8-bit fixed-point processing, resulting in negligible loss of floating-point accuracy. Resource utilization on the PYNQ-Z2 reached 78.37% for LUTs and 77.14% for BRAM, whereas on the ZCU-104, it was 49.76% for LUTs and 95.19% for BRAM. Real-time latency was as low as 1.26 $��\mu �s�$ per pixel (ZCU-104, Pavia Centre dataset) with a throughput of up to 6.35 MBPS. The proposed architecture provides scalable and high-performance solutions for onboard HSI classification under real-time constraints.

The ability to generate facial expressions is essential for humanoid social robots to engage in natural, human-like interactions. This capability significantly enhances the fluidity of human-robot communication and the precision of emotional expression. However, current methods rely heavily on pre-programmed behavioral patterns, which are manually implemented at considerable cost in both time and human labor. To enable humanoid robots to autonomously acquire generalized expressive capabilities, they must learn human-like expressions via self-supervised training. To address this challenge, we present a highly biomimetic robotic face equipped with physically actuated electronic facial units, alongside an end-to-end learning framework that integrates Kolmogorov-Arnold Networks (KAN) with attention mechanisms. In contrast to previous approaches, we have also developed an automated data collection system guided by expert-designed facial motion primitives, enabling the construction of a high-quality dataset. Notably, to the best of our knowledge, this constitutes the first facial expression dataset specifically designed for humanoid social robots. Extensive evaluations demonstrate that our method enables accurate and diverse facial mimicry across a range of test subjects.

This paper proposes an intelligent and adaptive security framework for internet of things (IoT) environments by integrating edge Honeypots, AI-driven intrusion detection systems (IDS), and software-defined networking (SDN). The system is designed to enhance real-time threat detection, reduce false positives, and dynamically mitigate attacks. Edge Honeypots are deployed at the network perimeter to attract and analyze malicious traffic, which is then used to train AI-based IDS models. The IDS employ a hybrid detection mechanism combining signature-based and anomaly-based techniques. SDN facilitates centralized traffic control and dynamic rule updates, enabling rapid responses to new attack vectors. The framework is implemented and evaluated in a simulated SDN-IoT environment using Mininet, with several machine learning models benchmarked. The Decision Tree model achieves the highest detection accuracy (97%) for IoT threats. Experimental results demonstrate improved detection performance, reduced false positives, and enhanced adaptability through a continuous learning loop between the IDS and honeypots.

Small object detection, a critical technique for recognizing and localizing diminutive targets in visual data, plays a vital role in applications ranging from remote sensing and unmanned aerial vehicle (UAV) vision to autonomous driving. Current methodologies, however, face substantial challenges, including detection accuracy limitations, low-resolution image processing difficulties, background noise interference, and target occlusion issues. To address these challenges, we propose FMF-DETR, an innovative small object detection framework featuring frequency-domain feature optimization through three key components: (1) High-Low Frequency Fusion Model (HLFM), (2) Focused Diffusion Feature Pyramid Network (FDFPN), and (3) BiPathNet (BPNet). Specifically, the HLFM module enhances multiscale feature representation by emphasizing high-frequency details while suppressing low-frequency background noise. The FDFPN architecture improves detection performance in complex scenarios through multiscale feature fusion and saliency-aware diffusion. BPNet introduces a dual-path feature extraction mechanism that simultaneously enhances feature discriminability and reduces computational overhead. Through the synergistic integration of these components, the proposed framework enhances both detection accuracy and operational efficiency. Comprehensive evaluations on the VisDrone dataset demonstrate FMF-DETR's superior performance, achieving a 2.2% accuracy improvement while reducing model parameters by 13.03M and computational complexity by 97.2G FLOPs compared to baseline methods. These results validate both the effectiveness and efficiency of our proposed framework.

Accurate fuel consumption prediction is critical for effective supply chain management and decision-making in industrial engineering systems. In price commitment scenarios, the discrepancies between market prices and transaction prices necessitate the development of scenario-specific prediction methods. However, such scenarios present several challenges. Market price fluctuations can induce variations in consumption patterns, while transaction price dynamics often differ from those in traditional markets, rendering conventional prediction models inadequate. Additionally, the cold-start characteristic of business results in limited historical data, and traditional methods relying on random sampling strategies often neglect sample heterogeneity, leading to biased weight allocation. To address these issues, we propose an Adaptive Sampling LightGBM framework (ASL), which integrates an adaptive sampling strategy and post-sampling bagging to enhance model stability with limited samples. By constructing a scenario-oriented risk function, the method incorporates market price volatility intensity and the divergence between commitment and conventional scenarios as dynamic weighting factors, thereby quantifying the time-varying impact of price elasticity on consumption. Experimental results using data from two fuel stations in North China demonstrate that our method outperforms benchmark prediction methods.

To address the challenges of coarse-grained access control, collusion attack risks, and massive data storage issues in cross-departmental traffic data sharing within Intelligent Transportation Systems (ITS) scenarios, this study proposes an Attribute-Based Conditional Proxy Re-Encryption (AB-CPRE) scheme integrated with blockchain technology. This scheme employs a conditional proxy re-encryption mechanism to achieve fine-grained access control based on device attributes and access policies, thereby defending against collusion attacks by proxy nodes and malicious users. By combining the distributed ledger of blockchain and IPFS's distributed storage of IPFS, a verifiable system is constructed that includes ciphertext hashes, a complete set of device attributes, and operational conditions. This ensures data integrity while reducing the computational and storage pressure on edge servers. Security analysis demonstrates that the scheme satisfies adaptive IND-CCA security under the standard model, and performance evaluation indicates significant improvements in computational efficiency and communication overhead compared with similar schemes.

In the current panorama of digital healthcare evolution and the proliferation of electronic health records (EHRs), healthcare systems face many challenges. These challenges encompass data administration and security, data exchange, and ensuring the confidentiality of medical data. Within this context, blockchain technology emerges as a promising way to address the numerous challenges associated with EHRs. Ensuring the security and confidentiality of EHRs remains a pivotal concern encompassing healthcare service recipients and providers. The compromise of a healthcare system leads to the exposure of intricately private health information. Typically stored in centralized databases, this information repository introduces susceptibilities that consequently fuel instances of cyber intrusion. To ensure the safeguarding of medical data, this research paper strategically adopts the Hyperledger Fabric (HLF) blockchain platform, which incorporates attribute-based access control mechanisms to thwart any malicious attempt at unauthorized access to sensitive information. These apprehensions regarding security have been effectively addressed by leveraging robust cryptographic protocols such as the AES-256 algorithm. This algorithm encrypts messages, transmitting the encrypted data across the network, thereby restricting visibility solely to intended recipients and providing a secure data exchange. The effectiveness of our proposed solution is rigorously evaluated using the Hyperledger Caliper tool. The evaluation encompasses pivotal performance metrics, average latency, throughput, success rate, resource consumption, and traffic.

Multimodal sentiment analysis (MSA) has recently encountered two major challenges: non-textual modalities are often affected by noise, and sentiment intensity differences are difficult to capture. To address these issues, we propose a Sentiment Intensity Contrastive Text-Enhanced Fusion Network (SICTEF Net), which achieves deep collaboration among text, audio, and visual modalities through three key mechanisms. First, a grouped-channel-attention based Feature Enhancement Module (EMA) is designed to mitigate modality-specific noise and emphasize emotion-sensitive cues by combining spatial–channel interaction mapping with dual-branch attention fusion. Second, a text-centered cross-modal fusion mechanism is introduced, where bidirectional multi-head self-attention and a residual-enhanced encoder jointly enable complementary mappings between text and non-text modalities, thereby producing intermediate representations that preserve semantic primacy while incorporating fine-grained complementary information. Third, a sentiment-intensity weighted contrastive learning strategy dynamically assigns weights to positive and negative sample pairs according to their sentiment intensity differences, allowing the model to more precisely distinguish samples with varying degrees of similarity in the embedding space. Experimental evaluation on the CMU-MOSI and CMU-MOSEI datasets demonstrates that SICTEF Net consistently outperforms state-of-the-art baselines in binary accuracy, F1 score, seven-class accuracy, mean absolute error (MAE), and Pearson correlation. Comprehensive ablation studies further confirm the complementary benefits of EMA, the text-enhanced Transformer, and sentiment-intensity contrastive learning. These results indicate that combining text-driven deep interaction, non-text modality enhancement via channel attention, and contrastive learning can improve the accuracy and robustness of multimodal sentiment analysis.

Mobile application recommendation has emerged as a pivotal domain within the realm of personalized recommendation systems. Traditional mobile application sequence recommendation approaches are predominantly dedicated to the pursuit of sophisticated sequence encoders to achieve more precise representations. However, existing sequence recommendation methods primarily consider the sequential order of historical App interactions, overlooking the time intervals between applications. This oversight hinders the model's capability to fully unearth the temporal correlations in user behavior, consequently limiting the accuracy and personalization of mobile application recommendations. Moreover, the interactions between users and mobile applications are typically sparse, which weakens the model's generalization capabilities. To address these issues, we propose a novel method for mobile application sequence recommendation, incorporating time interval-aware attention and contrastive learning (called Ti-CoRe). Specifically, this approach introduces a novel sequence augmentation strategy based on similarity replacement within a contrastive learning framework. By considering textual similarities between applications, this method selectively replaces applications that possess lower similarity scores to generate augmented sequences, increasing the diversity of the sample space and mitigating data sparsity. Furthermore, integrating a time interval-aware mechanism into the BERT4Rec model, the paper presents a new T-BERT encoder. It precisely assesses the influence of fluctuating time intervals on the prediction of the subsequent mobile application, thereby ensuring a more nuanced app representation. Experiments conducted on the 360APP real dataset demonstrate that Ti-CoRe consistently outperforms various baseline models in terms of NDCG and HR metrics.