The rapid advancements in Vision Language Models (VLMs) have prompted the development of multi-modal medical assistant systems. Despite this progress, current models still have inherent probabilistic uncertainties, often producing erroneous or unverified responses-an issue with serious implications in medical applications. Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning. However, these training-dependent strategies are costly and still lack sufficient alignment with clinical expertise. To address these issues, we propose an expert-in-the-loop framework named Expert-Controlled Classifier-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training. This framework introduces an uncertainty estimation strategy to identify unreliable outputs. It then retrieves relevant references to assist experts in highlighting key terms and applies classifier-free guidance to refine the token embeddings of MedVLM, ensuring that the adjusted outputs are correct and align with expert highlights. Evaluations across three medical visual question answering benchmarks demonstrate that the proposed Expert-CFG, with 4.2B parameters and limited expert annotations, outperforms state-of-the-art models with 13B parameters. The results demonstrate the feasibility of deploying such a system in resource-limited settings for clinical use.

In hardware-aware neural architecture search (NAS), accurately assessing a model’s inference efficiency is crucial for search optimization. Traditional approaches, which measure numerous samples to train proxy models, are impractical across varied platforms due to the extensive resources needed to remeasure and rebuild models for each platform. To address this challenge, we propose a multihardware-aware NAS method that enhances the generalizability of proxy models across different platforms while reducing the required sample size. Our method introduces a multihardware adaptive latency prediction (MHLP) model that leverages one-hot encoding for hardware parameters and multihead attention mechanisms to effectively capture the intricate interplay between hardware attributes and network architecture features. Additionally, we implement a two-stage sampling mechanism based on probability density weighting to ensure the representativeness and diversity of the sample set. By adopting a dynamic sample allocation mechanism, our method can adjust the adaptive sample size according to the initial model state, providing stronger data support for devices with significant deviations. Evaluations on NAS benchmarks demonstrate the MHLP predictor’s excellent generalization accuracy using only 10 samples, guiding the NAS search process to identify optimal network architectures.

In the edge computing environment, the uneven geographical distribution of tasks may lead to unbalanced load on the edge server. In addition, some larger tasks are difficult to completely offload to edge servers, which cannot fully utilize edge server resources. To solve the above problems, we propose a task scheduling method to minimize the completion time by combining the horizontal edge collaboration and fine-grained task partial offloading technology. First, combining horizontal edge collaboration and fine-grained task partial offloading technology, considering the location relationship between users and edge servers in multiuser multiedge server scenario, a task partial offloading optimization problem is established to minimize task completion time. Second, due to the nonconvex and variables coupling, we decompose the original problem into resource allocation, user-server association, and offloading strategy subproblems. A task scheduling algorithm based on improved teaching-learning-based optimization (ITLBO) is proposed to obtain the best task scheduling decision which includes task offloading location and offloading ratio. Simulation results show that the proposed method can effectively reduce the task completion time in edge collaboration environment.

In edge computing environments, the co-location of latency-critical (LC) services and best-effort (BE) jobs is a key strategy for enhancing resource utilization. However, existing analysis-based co-location strategies incur high analytical costs and struggle to rapidly adapt to the evolving fields of edge computing and microservice architectures, often failing to effectively meet the demands of edge computing environments. Feedback-based co-location strategies, while reducing analytical overhead, lack sufficient research in multi-node environments, resulting in overly coarse-grained deployment strategies. These strategies do not adequately consider the dynamic workloads and resource constraints inherent in edge computing, leading to improper resource allocation and degraded performance. This paper introduces Cortex, a Kubernetes-based co-location framework for edge device clusters that addresses these challenges by innovatively transforming the co-location deployment problem into a Minimum Cost Maximum Flow (MCMF) problem and employing the Network Simplex Algorithm (NSA) to optimize resource allocation and ensure QoS of LC services. Cortex also features a dynamic adjustment mechanism that adapts to changes in the request load of LC services, thereby minimizing the performance loss of BE jobs and reducing resource wastage. Our experiments in real edge device clusters demonstrate that Cortex significantly improves system resource utilization by 12.81%, increases the QoS satisfaction rate by 17.86%, and boosts the number of BE jobs by 51.96% compared to existing methods.

Deep neural networks (DNNs) have been widely used in remote sensing but demonstrated to be vulnerable with adversarial examples. By adding elaborately designed perturbations on the clean images, DNNs may output wrong prediction. Research on adversarial attack contributes to the study of model robustness. However, previous methods mainly focus on white-box scenario or digital domain for classification tasks, while the vulnerability of remote sensing detectors has not been fully explored. Aiming at attacking black-box remote sensing detectors in physical domain, we propose to generate a transferable physical adversarial patch (TPAP) as the perturbations. Specifically, the initial patch is optimized by a U-Net and modified by the plane mask and position mask before applied to the clean image. By attacking a surrogate model, TPAP can be transferred to the target model. Abundant experimental results validate the attack ability of TPAP and evaluate the robustness of current one-stage detectors.

Microservice architecture is a design philosophy that achieves decoupling by decomposing a monolithic application into multiple lightweight microservices. Meanwhile, edge computing can significantly reduce service latency and network congestion by extending computation and storage resources to the network edge. Therefore, in the microservice-oriented edge computing platform, a fundamental problem is how to efficiently deploy microservices with complex dependencies on the resource-constrained edge servers to satisfy the Quality of Service (QoS) constraints of users. Most of the existing studies ignore multiple call graphs with differentiated dependencies for an application, which often result in the violation of QoS. To address this issue, in this article, we first model the request response time of multiple instances and multiple call graphs scenario with service conflicts. Then, different from the existing heuristic or approximation algorithms which rely heavily on expert knowledge, we propose a graph-reinforcement-learning-based deployment (GRLD) framework. GRLD uses a graph convolutional network (GCN) to extract the graph data required for multiple call graphs with messages passing and aggregation, and the generated feature is fed into the underlying network of deep-reinforcement-learning (DRL). Experimental results show that GRLD outperforms counterparts in reducing service deployment overhead while satisfying QoS constraints of multiple call graphs.

For Heterogeneous Multi-Processor System-on-Chips (HMPSoCs), a reasonable pipeline design can significantly improve the inference performance of Deep Learning (DL) models. The pipeline design optimization can be modeled as a search problem where an accurate prediction model can efficiently speed up the search process. However, the performance prediction of DL models for the pipeline inference strategy is challenging because of the inter-layer effect, inference details, and variety of model structures. In this paper, we propose TPPNet, a transformer-based model for predicting the inference performance of various DL models with the pipeline inference strategy. TPPNet represents the DL model as an execution sequence with operators and hardware details to extract the hidden factors between layers. Moreover, we apply the Multi-task Learning (MTL) method to accurately predict throughput and latency metrics by constructing a predictive model. To the best of our knowledge, this is the first study dedicated to pipeline inference performance prediction for the DL model on HMPSoCs. We evaluate TPPNet on six well-known DL models using RK3399. The experimental outcomes affirm the high accuracy of TPPNet and its capability to significantly reduce the time overhead associated with pipeline exploration.

By deploying computing, storage, and bandwidth resources at the user side, vehicular edge computing (VEC) provides low-delay services for vehicle users. However, due to the limited resources of edge servers, how to efficiently meet the Quality-of-Service (QoS) requirements of multiple tasks and save the total energy consumption in a dynamic environment is an important issue in VEC. In this article, we first propose an energy consumption and QoS-aware co-offloading model. Unlike most previous studies, our goal is to minimize the total energy consumption while guaranteeing the QoS constraints of tasks, thus avoiding the overallocation of resources and high energy consumption caused by the one-sided pursuit of delay minimization. Then, without the requirements for domain experts, we propose Bayesian optimization-based computation offloading (BOCO) method to find the optimal offloading decision. To the best of our knowledge, this work is the first to apply Bayesian optimization to computation offloading in VEC. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithm. Experimental results verify that our proposed BOCO outperforms counterparts.

Neural network quantization techniques play an important role in efficiently deploying deep learning models on the hardware with limited computing and storage resources. Numerous applications of this technology, such as autopilot, necessitate not just efficiency but also robustness. Research on the robustness of quantized networks against adversarial attacks is becoming one of the major points of interest. In this work, we rethink the impact of quantization on adversarial attacks and explore the boundary of the robustness of quantized neural networks. This study reveals that activation quantization can be used as a defense to weaken adversarial noise, but the robustness of quantized models is still limited by the amplification effect of network errors, including quantization errors and adversarial noise. To address this problem, we propose the quantization adversarial noise suppression (QANS) method that employs a Gaussian kernel regularization constraint to stabilize the model by restricting the perturbation error within two levels of tolerance. Extensive experiments are conducted with Wide ResNet and VGG-16 models on CIFAR-10 and street view house number datasets under different attack methods, including several white-box and black-box attacks. Experimental results show the proposed method achieves superior robustness to prior works.

Convolutional neural networks (CNNs) have been widely utilized to identify hand gestures from surface electromyography (sEMG) signals. However, due to the nonstationary characteristics of sEMG, the classification accuracy usually degrades significantly in the daily living environment involving complex hand movements. To further improve the reliability of a classifier, unconfident classifications are expected to be identified and rejected. In this study, we propose a novel approach to estimate the probability of correctness for each classification. Specifically, a confidence estimation model is established to generate confidence scores (ConfScore) based on posterior probabilities of CNN, and an objective function is designed to train the parameters of this model. In addition, a comprehensive metric that combines the true acceptance rate (TAR) and the true rejection rate (TRR) is proposed to evaluate the rejection performance of ConfScore, so that the tradeoff between system security and control lag could be fully considered. The effectiveness of ConfScore is verified using data from public databases and our online platform. The experimental results illustrate that ConfScore can better reflect the correctness of CNN classifications than traditional confidence features, i.e., maximum posterior probability and entropy of the probability vector. Moreover, the rejection performance is observed to be less sensitive to variations in rejection thresholds.

To develop multi-functionalhuman-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.

The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.

The participant's reputation is vital to improve the quality of service for Mobile Crowd Sensing (MCS). A multidimensional reputation evaluation model was proposed in this paper to evaluate the participant's reputation more objectively. Different from the existing strategies, the service delay and the count of the successful as well as the failed transactions were additionally utilized to evaluate the participant's reputation. An algorithm based on Analytic Hierarchical Process (AHP) was presented to establish the reputation evaluation weight matrix. Besides, a fuzzy logic based mechanism was proposed to normalize the value of the four criteria and a dual-threshold mechanism was designed to achieve admission control more properly. Finally, extensive simulations were conducted and the simulation results confirmed the effectiveness of the reputation evaluation model.