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Abstract: Communication is crucial to the performance of distributed training. Today’s solutions tightly couple the control and data planes and lack flexibility, generality, and performance. In this study, we present SDCC, a software-defined collective communication framework for distributed training. SDCC is based on the principle of modern systems design to effectively decouple the control plane from the data plane. SDCC abstracts the operations for collective communication in distributed training with dataflow operations and unifies computing and communication with a single dataflow graph. The abstraction, together with the unification, is powerful: it enables users to easily express new and existing collective communication algorithms and optimizations, simplifies the integration with different computing engines (e.g., PyTorch and TensorFlow) and network transports (e.g., Linux TCP and kernel bypass), and allows the system to improve performance by exploiting parallelism exposed by the dataflow graph. We further demonstrate the benefits of SDCC in four use cases. PubDate: 2024-07-31 DOI: 10.1007/s11432-023-3894-4
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Counterfactual explanations provide explanations by exploring the changes in effect caused by changes in cause. They have attracted significant attention in recommender system research to explore the impact of changes in certain properties on the recommendation mechanism. Among several counterfactual recommendation methods, item-based counterfactual explanation methods have attracted considerable attention because of their flexibility. The core idea of item-based counterfactual explanation methods is to find a minimal subset of interacted items (i.e., short length) such that the recommended item would topple out of the top-K recommendation list once these items have been removed from user interactions (i.e., good quality). Usually, explanations are generated by ranking the precomputed importance of items, which fails to characterize the true importance of interacted items due to separation from the explanation generation. Additionally, the final explanations are generated according to a certain search strategy given the precomputed importance. This indicates that the quality and length of counterfactual explanations are deterministic; therefore, they cannot be balanced once the search strategy is fixed. To overcome these obstacles, this study proposes learning-based counterfactual explanations for recommendation (LCER) to provide counterfactual explanations based on personalized recommendations by jointly modeling the factual and counterfactual preference. To achieve consistency between the computation of importance and generation of counterfactual explanations, the proposed LCER endows an optimizable importance for each interacted item, which is supervised by the goal of counterfactual explanations to guarantee its credibility. Because of the model’s flexibility, the trade-off between quality and length can be customized by setting different proportions. The experimental results on four real-world datasets demonstrate the effectiveness of the proposed LCER over several state-of-the-art baselines, both quantitatively and qualitatively. PubDate: 2024-07-25 DOI: 10.1007/s11432-023-3974-2
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Abstract: Abstract Emotion recognition in conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. In this paper, we propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC tasks. Our EmotionIC consists of three main components, i.e., identity masked multi-head attention (IM-MHA), dialogue-based gated recurrent unit (DiaGRU), and skip-chain conditional random field (SkipCRF). Compared to previous ERC models, EmotionIC can model a conversation more thoroughly at both the feature-extraction and classification levels. The proposed model attempts to integrate the advantages of attention- and recurrence-based methods at the feature-extraction level. Specifically, IMMHA is applied to capture identity-based global contextual dependencies, while DiaGRU is utilized to extract speaker- and temporal-aware local contextual information. At the classification level, SkipCRF can explicitly mine complex emotional flows from higher-order neighboring utterances in the conversation. Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets. The ablation studies confirm that our modules can effectively model emotional inertia and contagion. PubDate: 2024-07-25 DOI: 10.1007/s11432-023-3908-6
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Abstract: Abstract High-energy radiation detection and imaging technology has significant applications in high-energy physics research, medical imaging, and industrial monitoring. Lead-free metal halides exhibit exceptional potential for conducting indirect detection of high-energy radiation due to their characteristics of low toxicity, strong stability, high light yield, and large Stokes shift. This paper reviews the most recent advances in lead-free metal halide scintillator materials for X-ray imaging. Subsequently, it lists the most important parameters of scintillator performance and introduces the production procedures for single crystal, powder, and nanocrystal scintillators. Furthermore, it discusses the manufacturing of scintillator films with improved performance, focusing on large-area flexible scintillator films and the coupling with microstructures. Finally, it discusses current challenges and opportunities for enhancing X-ray imaging using lead-free metal halide scintillator materials. PubDate: 2024-07-25 DOI: 10.1007/s11432-024-4057-0
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Abstract: Abstract In this paper, a resilient tracking control scheme with cooperative collision avoidance performance is studied for the fixed-wing unmanned aerial vehicle (UAV) leader-follower formation in the presence of actuator failures and external disturbances. Firstly, based on the control objectives of UAV formation tracking and collision avoidance, the transformation tracking errors are obtained using the prescribed performance control technique. Next, a fault detection mechanism is introduced to determine if there is the actuator fault. Subsequently, the event-triggered resilient fault observers are designed based on a dynamic event-triggered mechanism to estimate actuator faults. Furthermore, the prescribed performance functions and the H∞ performance index are employed to ensure the UAV formation collision-free and mitigate the impact of disturbances. Moreover, the resilient controller is designed to minimize the effect of the perturbations for the control gain and the fault observer gain on the system. The stability of the system is also proven through the Lyapunov stability analysis, and the controller gains are calculated by solving the linear matrix inequality. Finally, the validity of the proposed control strategy is demonstrated by the simulation analysis. PubDate: 2024-07-25 DOI: 10.1007/s11432-023-4099-x
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Abstract: Abstract Securing a comfortable, wearable compact frequency beam scanning antenna (FBSA) with robustness to deformation, low specific absorption rate (SAR), and good coverage of the surrounding environment for Internet of Things (IoT) applications, such as on-body navigation and wireless communication is an emerging challenge. In this work, a robust textile-based spoof plasmonic frequency scanning antenna utilizing higher-order modes is presented, which is also robust to deformation caused by the activities of the human body. The innovative design of the element ensures the high-efficiency transmission of the fundamental mode of spoof surface plasmon polaritons (SSPP) structure, providing the potential of being a multifunctional composite device in the compact on-body network. Besides, an artificial magnetic conductor (AMC) is designed underneath the SSPP structure, obtaining a low SAR value (0.113 W/kg), which ensures the safety of users. As a practical realization of this concept, a textile-based spoof plasmonic antenna was fabricated in the microwave regime and the performed experimental results show the proposed antenna has a single-beam radiation characteristic with a 70° beam scanning angle range when the frequency is 4.7–6.0 GHz with a high average realized gain of 13.15 dBi. And it still maintains a steady performance when faced with structure deformation, which proves its robustness. Wireless communication quality experiments are performed to demonstrate the proposed antenna can measure the angles of targets and realize wireless signal transmission to specific targets as the frequency varies, it may find great potential in the field of on-body IoT applications. PubDate: 2024-07-25 DOI: 10.1007/s11432-024-4049-5
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Abstract: Abstract A multispectral imaging system often cannot capture 3D spatial information owing to hardware limitations, which diminishes the effectiveness across various domains. To address this problem, we have developed a multispectral stereo imaging system along with an adaptive 3D reconstruction algorithm. Unlike existing unmanned aerial vehicle stereo imaging systems, our multispectral stereo imaging system uses two multispectral cameras with asymmetric spectral bands positioned at different angles. This design enables the acquisition of a higher number of bands and lateral spatial information while maintaining a lightweight structure. This system introduces challenges such as large geometric distortions and intensity differences between multiple bands. To accurately recover 3D spatial information, we propose an adaptive 3D reconstruction method. This method employs a position and orientation system-assisted projection transformation and a normalized threshold adjustment strategy. Finally, mutual information is used to reconstruct the multispectral images densely, effectively addressing nonlinear differences and generating a comprehensive multispectral point cloud. Our stereo system was used for two real data collections in different regions, and the efficacy of the proposed 3D reconstruction method was validated by comparing it with existing methods and commercial software. PubDate: 2024-07-25 DOI: 10.1007/s11432-024-4056-8
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Abstract: Abstract Racing drones have attracted increasing attention due to their remarkable high speed and excellent maneuverability. However, autonomous multi-drone racing is quite difficult since it requires quick and agile flight in intricate surroundings and rich drone interaction. To address these issues, we propose a novel autonomous multi-drone racing method based on deep reinforcement learning. A new set of reward functions is proposed to make racing drones learn the racing skills of human experts. Unlike previous methods that required global information about tracks and track boundary constraints, the proposed method requires only limited localized track information within the range of its own onboard sensors. Further, the dynamic response characteristics of racing drones are incorporated into the training environment, so that the proposed method is more in line with the requirements of real drone racing scenarios. In addition, our method has a low computational cost and can meet the requirements of real-time racing. Finally, the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in a series of simulations and real-world experiments. PubDate: 2024-07-25 DOI: 10.1007/s11432-023-4029-9
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Abstract: Abstract With the growing demand for automation in agriculture, industries increasingly rely on drones to perform crop monitoring and surveillance. In this regard, fixed-wing unmanned aerial systems (UASs) are viable platforms for scanning a large crop field, given their payload capacity and range. To achieve maximum coverage without landing for battery replacement, an algorithm for producing a minimal required energy survey path is essential. Hence, an energy-aware coverage path planning algorithm is proposed herein. The constraints for a fixed-wing UAS to fly at low altitudes while achieving full coverage of the crop field are first analyzed. Then, the full path is decomposed into straight-line and U-turn primitives. Finally, an algorithm to calculate a combination of straight-line segments and U-turns is proposed to obtain the path with minimum required energy consumption. The genetic algorithm is used to efficiently determine the order of the straight-line paths to traverse. Case studies show that the proposed algorithm can produce planning results for a convex-polygon-shaped crop field. PubDate: 2024-07-25 DOI: 10.1007/s11432-023-4087-4
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Conclusion We present a wavelet-domain feature-decoupling Transformer-based tracking network for the weakly supervised MOT task (FDMOT). Our FDMOT has two improvements over the previous weakly supervised methods. First, FDMOT decouples noisy intermediate features caused by noisy pseudo identity labels in the wavelet domain, extracting discriminative features for accurately detecting and identifying multiple targets. Second, FDMOT further improves the noise-decoupled embedding features into the well-refined ones with the cooperation of the three feature-decoupling Transformer-based branches, which can accurately identify and track heavily occluded targets in crowded scenes. Experimental results show the superiority of FDMOT compared with several state-of-the-art supervised and weakly supervised MOT methods. PubDate: 2024-07-24 DOI: 10.1007/s11432-022-4097-y
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Abstract: Abstract The lower power object detection challenge (LPODC) at the IEEE/ACM Design Automation Conference is a premier contest in low-power object detection and algorithm (software)-hardware co-design for edge artificial intelligence, which has been a success in the past five years. LPODC focused on designing and implementing novel algorithms on the edge platform for object detection in images taken from unmanned aerial vehicles (UAVs), which attracted hundreds of teams from dozens of countries to participate. Our team SEUer has been participating in this competition for three consecutive years from 2020 to 2022 and obtained sixth place respectively in 2020 and 2021. Recently, we achieved the championship in 2022. In this paper, we presented the LPODC for UAV object detection from 2018 to 2022, including the dataset, hardware platform, and evaluation method. In addition, we also introduced and discussed the details of methods proposed by each year’s top three teams from 2018 to 2022 in terms of network, accuracy, quantization method, hardware performance, and total score. Additionally, we conducted an in-depth analysis of the selected entries and results, along with summarizing representative methodologies. This analysis serves as a valuable practical resource for researchers and engineers in deploying the UAV application on edge platforms and enhancing its feasibility and reliability. According to the analysis and discussion, it becomes evident that the adoption of a hardware-algorithm co-design approach is paramount in the context of tiny machine learning (TinyML). This approach surpasses the mere optimization of software and hardware as separate entities, proving to be essential for achieving optimal performance and efficiency in TinyML applications. PubDate: 2024-07-24 DOI: 10.1007/s11432-023-3958-4
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Abstract: Abstract During short-range air combat involving unmanned aircraft vehicle (UAV) swarms, UAVs must make accurate maneuver decisions based on information from both enemy and friendly UAVs. This dual requirement of competition and cooperation presents a significant challenge in the field of unmanned air combat. In this paper, a method based on multi-agent reinforcement learning (MARL) is proposed to address this issue. An actor network containing three subnetworks that can handle different types of situational information is designed. Hence, the results from simpler one-on-one scenarios are leveraged to enhance the complex swarm air combat training process. Separate state spaces for local and global information are designed for the actor and critic networks. A detailed reward function is proposed to encourage participation. To prevent lazy participants in air combat, a reward assignment operation is applied to distribute these dense rewards. Simulation testing and ablation experiments demonstrate that both the transfer operation and reward assignment operation can effectively deal with the swarm air combat scenario, and reflect the effectiveness of the proposed method. PubDate: 2024-07-24 DOI: 10.1007/s11432-023-4088-2
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Abstract: Abstract The objective of the invariant ellipsoid method is to minimize the smallest invariant and attractive set of a linear control system operating under the influence of bounded external disturbances. This study extends the application of this method to address the leader-following consensus problem. Initially, a linear control protocol is designed for the multi-agent system in the absence of disturbances. Subsequently, in the presence of bounded disturbances, by employing a similar linear control protocol, a necessary and sufficient condition is introduced to derive the optimal control parameters for the multi-agent system such that the state of followers converges to and remains in a minimal invariant ellipsoid around the state of the leader. PubDate: 2024-07-24 DOI: 10.1007/s11432-023-4042-1
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Abstract: Abstract Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally, these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper, we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division, comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4 × faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range. PubDate: 2024-07-23 DOI: 10.1007/s11432-023-4013-x
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Abstract: Abstract Covert communication has been widely investigated to avoid the transmission behavior being overheard by the warder. However, covert communication may be illegitimately utilized by unauthorized parties to evade the supervision of authorized agencies, which leads to great challenges to information security. To meet the need for authorized parties to monitor and prevent illegitimate transmission between unauthorized nodes, a novel paradigm, called legitimate monitor, is proposed for counter covert communications. In the preceding covert communication system, the covert transmission rate is the focus. Differently, the core concern of the legitimate monitor system is the outage probability of the transmission between unauthorized nodes, which should be maximized to interrupt the potential but undetectable transmission. To achieve these goals effectively, a proactive guarding approach is proposed, where the authorized warder detects the transmission behavior and emits jamming signals to interfere with the potential transmission, simultaneously. In particular, the jamming power at the warder is optimized under cases where the instantaneous/statistical channel state information is available. Besides, the corresponding outage probability is derived to evaluate the system performance, which can also be simplified to scenarios with a passive warder. Numerical results demonstrate that proactive guarding outperforms the passive one, especially when the warder is not proximal to the unauthorized transmitter. In addition, the proposed jamming power allocation scheme also outperforms other benchmark schemes. PubDate: 2024-07-23 DOI: 10.1007/s11432-023-4025-2
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Abstract: Abstract Accurate identification of malicious traffic is crucial for implementing effective defense counter-measures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false positives. Nevertheless, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector (ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training: intraclass consistency enhanced (ICE) representation learning and Class-Perception (CP) detector construction. In the first module, we employ a variational autoencoder (VAE) in conjunction with Kullback-Leibler (KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for non-drifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP detector to detect the potential drifting samples using the centroid and threshold defined for each class. We evaluate ICE-CP and some advanced methods on various real-world malicious traffic datasets. The results show that our method outperforms others in identifying malicious traffic and detecting potential drifting samples, demonstrating outstanding robustness among different concept drift settings. PubDate: 2024-07-23 DOI: 10.1007/s11432-023-4010-4
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Abstract: Conclusion We first report a 2D material-based P-FET with excellent output current saturation characteristics and demonstrate the highest small-signal output impedance characteristics among all previously published 2D-FETs. Further, we utilize the excellent performance of the device to demonstrate a current mirror circuit, which has better high precision current replication performance than silicon-based devices. This work provides a possible technical approach for the development of high-performance analog circuit devices based on 2D materials. PubDate: 2024-07-22 DOI: 10.1007/s11432-024-4083-6
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Abstract: Conclusion This study proposes a model named BioKG-CMI to predict CMIs based on a biological knowledge graph. Faced with limited data, we employ subcellular localization to generate negative samples that align more closely with biological logic. To mine semantic information in circRNA and miRNA sequences, we introduce the pre-trained model BERT to learn sequence feature representation. Guided by the hypothesis that adjacent molecules have similar functions, we calculate spatial proximity between nodes of the same class. The DisMult algorithm is applied to extract the potential logical rules of the knowledge graph and learn entity and relationship representations. Subsequently, the integration of multi-feature successfully addresses the challenge of expressing the complex biological knowledge graph and overcoming the limitation of single-feature inadequacy. Multiple comparative experiments and case studies demonstrate the robustness of the proposed model. PubDate: 2024-07-22 DOI: 10.1007/s11432-024-4098-3
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