Facilitating the collecting and sharing of heterogeneous scientific data among researchers is a key approach to unlocking the full value of data. For data platforms to gain widespread adoption, they must satisfy two criteria simultaneously: (1) allow non-technical researchers to submit personalized data autonomously, and (2) comply with the FAIR principles — Findable, Accessible, Interoperable, and Reusable. To meet these requirements, we propose the Dynamic Container (DC) paradigm, which enables heterogeneous data collection and transforms the data schema designers from database experts to non-technical end users. We further introduce a data representation model that decomposes schemas into familiar components for end users and supports dynamical schema construction through an intuitive drag-and-drop interaction method. We have folded these ideas into a system and evaluated the usability through user studies. The results indicate that our system makes it more usable, efficient and less error-prone for end users to customize schemas.

Recent advancements in reinforcement learning (RL) have driven progress across various domains; however, RL algorithms often struggle in complex multiagent environments due to challenges such as instability, low sample efficiency, and the curse of dimensionality. Hierarchical RL (HRL) provides a structured framework for decomposing complex tasks into more manageable subtasks, making it a promising approach for multiagent systems. In this article, we introduce a novel hierarchical architecture that autonomously generates effective subgoals without explicit constraints, thereby enhancing both training stability and adaptability. To further improve sample efficiency and adaptability, we propose a dynamic goal-generation strategy that adjusts subgoals in response to environmental changes. Additionally, we address the critical challenge of credit assignment in multiagent settings by integrating our hierarchical architecture with a modified QMIX network, thereby facilitating more effective strategy coordination. Extensive comparative experiments against state-of-the-art RL algorithms demonstrate that our approach achieves superior convergence speed and overall performance in multiagent environments. These results validate the effectiveness and flexibility of our method in handling complex coordination tasks. The implementation is publicly available at https://github.com/SICC-Group/GMAH

In this paper, we introduce a novel cooperative target tracking algorithm, namely the quantum-inspired belief propagation, aimed at rectifying the limitations observed in existing localization algorithms employed in multi-target cooperative tracking scenarios. Leveraging the principles of quantum superposition, our algorithm seeks to alleviate the uncertainty inherent in message fusion within belief propagation frameworks, thereby enhancing the accuracy and stability of multi-target cooperative localization. The utilization of the quantum Monte Carlo method facilitates the simulation of the message distribution process, with quantum particles embodying the superposition of multiple states concurrently. This approach effectively addresses the intractable integrations encountered in message updating on factor graphs, rendering the algorithm agnostic to the number of particles involved. Moreover, quantum unitary transformations and quantum black-box operations are deployed to encode factor graph function nodes for the propagation of quantum messages. This innovation surmounts the challenge posed by traditional factor graph function nodes’ inability to process quantum messages. Experimental findings corroborated the superiority of the proposed algorithm in terms of accuracy and robustness.

The rapid expansion of the Internet of Things (IoT) has brought forth new challenges and opportunities in securely managing and sharing vast amounts of data generated by connected devices. Blockchain technology, with its decentralization, tamper-resistance, and traceability, offers a promising framework for IoT data sharing but struggles to safeguard smart contracts and sensitive data. Integrating trusted execution environments (TEEs) with blockchain addresses these concerns, enabling secure execution and communication via remote attestation. However, existing remote attestation methods face challenges, including incompatibility across heterogeneous TEEs, inefficiency under frequent authentication, and vulnerability to DoS attacks. To tackle these, we propose a blockchain-based unified remote attestation scheme for IoT. Our three-tier blockchain architecture—comprising a certificate authority (CA) channel, an authoritative channel, and a business channel—separates authentication, attestation, and operations while ensuring auditability. An abstraction layer supports heterogeneous TEEs, and an authoritative blockchain stores authentication reports, enabling secure, frequent attestations. Additionally, a distributed CA system enhances resilience to DoS attacks. Experimental results validate our scheme’s efficiency and security, offering a robust solution for IoT data sharing.

In recent years, swarm robot systems have garnered increasing attention, both in the industry and academia. These collaborative systems demand effective solutions for data storage, sharing, and security to unlock their full potential. To address these needs, this paper introduces a comprehensive distributed storage and computation framework based on blockchain and federated learning technology. The framework enables real-time collaborative data storage and computation, ensuring the security and reliability of collective intelligence systems. For data storage, we combine blockchain and dynamic containers to achieve secure and efficient storage of diverse robot data. To facilitate secure data utilization and sharing among robots, we present a federated learning-based collaborative computation approach. It allows robots to exchange model parameters while safeguarding data security, providing a versatile collaborative computation framework for collective systems. To validate the security and resilience of our framework, we present a practical scenario involving multi-agent collaborative localization. We conduct a thorough evaluation of the performance and security of this collaborative localization system, offering valuable insights for researchers in the field of swarm robotics.

Accurate and real-time location tracking is vital for various applications in public safety and the military, particularly in search and rescue missions. Traditional filtering localization algorithms are more effective in linear environments and require precise initial estimates and system noise for optimal results. In complex and unreliable environments, these algorithms often yield poor localization results. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy of the localization algorithm. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. This approach reduces the system’s positioning errors and addresses credit allocation issues common in multi-agent reinforcement learning.

Material data forms the foundation of the Industrial Internet of Things (IIoT). The rapid advancement of big data technology has opened up new opportunities for material research and development, ushering in the era of data-driven paradigms. As the cornerstone for material genetic engineering technology, the material big data platform is expanding its data scale and facing an increasing demand for sharing in light of the continuous progress and widespread application of big data technology. However, this development also poses security challenges, including the risks of data leakage and tampering. To address these challenges, this article focuses on the National Materials Genetic Engineering Discrete Data Exchange Platform (MGED). It leverages blockchain technology to design a secure material big-data storage and sharing architecture, S-MBDA, ensuring the security and reliability of the material’s big data platform. Additionally, a verifiable retrieval scheme based on a two-layer index structure of bitmap and MPT tree is proposed to enhance the efficiency of blockchain-based retrieval. This scheme aims to guarantee the integrity of retrieval data while achieving efficient and accurate searches across heterogeneous data sources. Through integrating blockchain technology and adopting a novel retrieval scheme, the article presents a comprehensive approach to secure material data storage, sharing, and retrieval. The proposed architecture and scheme address the critical security concerns associated with material big data platforms and contribute to the efficient and accurate retrieval of heterogeneous data.

In modern navigation and positioning systems, accurate location information is crucial for ensuring system performance and user experience. Particularly, in scenarios involving the use of multiple agents such as robots and drones for rescue operations in unknown complex environments, accurate localization is fundamental for subsequent actions. However, traditional filtering-based localization algorithms may exhibit suboptimal performance and are sensitive to initial estimates and system noise. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. The main contributions of this paper are as follows: Firstly, a local localization estimation method based on reinforcement learning compensation Extended Kalman Filter (EKF) is proposed, which further corrects the results of the EKF algorithm and eliminates initial estimation errors. Secondly, a global collaborative localization estimation algorithm (MARL_CF) based on credit allocation in multi-agent reinforcement learning is proposed, which maximizes the reduction of overall localization error through information sharing and global optimization. Finally, the effectiveness of the proposed algorithms is validated through both numerical simulation and physical experiments. The results demonstrate that the proposed MARL_CF significantly improve the accuracy and robustness of localization in complex environments.

With the advent of the era of data-driven material R&D, more and more countries have begun to build material Big Data sharing platforms to support the design and R&D of new materials. In the application process of material Big Data sharing platforms, storage and retrieval are the basis of resource mining and analysis. However, achieving efficient storage and recovery is not accessible due to the multimodality, isomerization, discrete and other characteristics of material data. At the same time, due to the lack of security mechanisms, how to ensure the integrity and reliability of the original data is also a significant problem faced by researchers. Given these issues, this paper proposes a blockchain-based secure storage and efficient retrieval scheme. Introducing the Improved Merkle Tree (MMT) structure into the block, the transaction data on the chain and the original data in the off-chain cloud are mapped through the material data template. Experimental results show that our proposed MMT structure has no significant impact on the block creation efficiency while improving the retrieval efficiency. At the same time, MMT is superior to state-of-the-art retrieval methods in terms of efficiency, especially regarding range retrieval. The method proposed in this paper is more suitable for the application needs of the material Big Data sharing platform, and the retrieval efficiency has also been significantly improved.

Real-time high-accuracy localization has a wide range of applications in scenarios, such as pedestrian navigation, emergency rescue, and vehicle networks. In these conditions, the measurement models are often nonlinear, and traditional Kalman and particle filters cannot provide long-time high-precision location-based services. To this end, we propose a Gaussian condensation filter (GCF) algorithm that can achieve high-accuracy localization in a harsh environment. However, aiming at the degradation of sampling points in target tracking based on the GCF, this article proposes a GCF algorithm based on particle flow which transfers the sample points satisfying the prior distribution of the target state to the posterior distribution, thereby improving the practical accuracy of the target-tracking algorithm. Further, to enhance the information fusion in the cooperative network, we propose a multitarget cooperative tracking algorithm to accomplish spatially constrained timing filtering of state information for improving the error correction of the target nodes on timing estimation. Numerical simulations are conducted to determine the effectiveness of our proposed algorithms. Compared with the GCF, its positioning accuracy is improved to 44.6%. Compared with the Gaussian condensation algorithm based on particle flow (PF), the practical accuracy of the GCF algorithm based on cooperative constrained PF in multitarget tracking is improved to 58.1%.

Neural network processors and accelerators are domain-specific architectures deployed to solve the high computational requirements of deep learning algorithms. This article proposes a new instruction set extension for tensor computing, TCX, using Reduced Instruction Set Computer (RISC) instructions enhanced with variable length tensor extensions. It features a multi-dimensional register file, dimension registers, and fully generic tensor instructions. It can be seamlessly integrated into existing RISC Instruction Set Architectures and provides software compatibility for scalable hardware implementations. We present a tensor accelerator implementation of the tensor extensions using an out-of-order RISC microarchitecture. The tensor accelerator is scalable in computation units from several hundred to tens of thousands. An optimized register renaming mechanism is described that allows for many physical tensor registers without requiring architectural support for large tensor register names. We describe new tensor load and store instructions that reduce bandwidth requirements using tensor dimension registers. Implementations may balance data bandwidth and computation utilization for different types of tensor computations such as element-wise, depthwise, and matrix-multiplication. We characterize the computation precision of tensor operations to balance area, generality, and accuracy loss for several well-known neural networks. The TCX processor runs at 1 GHz and sustains 8.2 Tera operations per second using a 4,096 multiply-accumulate compute unit. It consumes 12.8 mm 2 while dissipating 0.46W/TOPs in TSMC 28-nm technology.

Chromosome structure plays an essential role in exploring the relationship between chromosome structure and gene transcription. In recent years, High-resolution Hi-C data allows us to explore the chromosome structures in 3D space. However, most of the existing chromosome structure reconstruction methods can not realize the 3D structure reconstruction at a high resolution (for example, 5 Kilobase (KB)). In this paper, we first proposed the NeRV-3D to reconstruct the 3D chromosome structure at a low resolution based on the nonlinear dimensionality reduction visualization algorithm. Then, we proposed the NeRV-3D-DC to reconstruct and visualize the 3D chromosome structure at a high resolution based on the divide-and-conquer method. Results show that NeRV-3D and NeRV-3DDC perform better than existing methods by comparing the 3D visualization effects and evaluation metrics on simulated and real Hi-C datasets. Implementation of NeRV-3D-DC is available on https://githuh.com/ghaiyan/NeRV-3D-DC

Neural network processors and accelerators are domain-specific architectures deployed to solve the high computational requirements of deep learning algorithms. This paper proposes a new instruction set extension for tensor computing, TCX, with RISC-style instructions and variable length tensor extensions. It features a multidimensional register file, dimension registers, and fully generic tensor instructions. It can be seamlessly integrated into existing RISC ISAs and provides software compatibility for scalable hardware implementations. We present an implementation of the TCX tensor computing accelerator using an out-of-order microarchitecture implementation. The tensor accelerator is scalable in computation units from several hundred to tens of thousands. An optimized register renaming mechanism is described which allows for many physical tensor registers without requiring architectural support for large tensor register names. We describe new tensor load and store instructions that reduce bandwidth requirements based on tensor dimensions. Implementations may balance data bandwidth and computation utilization for different types of tensor computations such as element-wise, depth-wise, and matrix-multiplication. We characterize the computation precision of tensor operations to balance area, generality, and accuracy loss for several well-known neural networks. The TCX processor runs at 1 GHz and sustains 8.2 Tera operations per second using a 4096 multiplication-accumulation compute unit with up to 98.83% MAC utilization. It consumes 12.8 square millimeters while dissipating 0.46 Watts per TOP in TSMC 28nm technology.

Abrupt-motion tracking is challenging due to the target’s unpredictable action. Although particle filter is suitable for target tracking of nonlinear non-Gaussian systems, it suffers from the problems of particle impoverishment and sample-size dependency. Inspired by quantum mechanics that one quantum bit could represent a superposition of two states, this paper proposes a diversity-preserving quantum-enhanced particle filter (DQPF). Firstly, we quantized the motion modes of particles into a superposition of several modes of motion, resulting in a quantum particle set that retains diversity. Aiming for the abruption of target motion, we propagate the quantum particles during the prediction stage. The quantum particles will already be in these possible positions even if abruption occurs, which addresses the abrupt-motion issue and reduces the tracking delay. Benefitting from quantum mechanics, the proposed particle filter has better precision and stability with fewer particles than the general particle filter. Compared to state-of-the-art, numerical experimental results demonstrate that the proposed DQPF has higher accuracy and stability under the same conditions, displaying superior performance to traditional modified particle filter methods.

Nowadays, the meter reading is detached into two parts: 1) image collection and 2) image recognition. Most previous researchers perceived meter reading as an image classification problem and obtained impressive classification performance. However, the numerical is also a critical metric of meter measurement and has not been noticed in previous research. This article redefines the meter reading issue as a hybrid procedure of regression and classification and creates a specific model. The resulting algorithm bespeaks the performance of measurement and recognition. The model consists of a hybrid regression and classification loss function and multibranch convolutional neural networks. We construct two data sets to validate the model: 1) normal data set and 2) carry data set, corresponding to classification and numeric accuracy dividedly. The experiments show that the model establishes new state-of-the-art metrics and achieves 0.5312 mean square error (MSE) on numerical precision and 99.98% accuracy on classification accuracy for 3-min training, by over 70 times on MSE and 0.11% on accuracy than the best performing model proposed by a recent study. Furthermore, a production-ready meter-reading system was deployed in a genuine factory with hybrid regression and classification multibranch convolutional neural networks, smart meter shells, and a set of cloud servers.

Genetic programming (GP) has a significant achievement in feature construction and non-overlap degree can help to improve the generalization ability of GP based feature construction. However, the non-overlap degree is biased towards the majority class. In this paper, a novel GP based feature construction method with augmented non-overlap degree is proposed to enhance the generalization ability for imbalanced data. And the constructed features are evaluated by a novel function based on the combination of the area under the ROC curve metric and the augmented non-overlap degree. The generalization performance is evaluated not only by a particular classification algorithm, but also by six widely used classification algorithms. The experiments conducted on five imbalanced biomedical datasets with different imbalance rates show that the proposed GP-AANO method can achieve superior generalization performance for classification.

Genetic programming (GP) has shown promising results in interpretable feature extraction, but few works considered both classification accuracy and data visualization as objectives. Evaluating the extracted features based on the combination of accuracy measures and visualization measures can help to achieve the two objectives simultaneously. However, the exploitation of improper visualization measures and combination methods will decrease the classification accuracy. In this paper, a novel feature extraction method based on GP and non-overlap degree is proposed to extract interpretable features for high accuracy and visualization. And a novel function that maximizes the product of the accuracy of a linear classifier and the non-overlap degree is proposed to evaluate the extracted features. The proposed method, named GP-ANO, is compared with other methods on five medical datasets by six common machine learning methods. The experimental results demonstrate that the GP-ANO method outperforms other compared methods in terms of both classification accuracy and data visualization.

Intrusion detection system (IDS) is a second line of the security mechanism for the wireless sensor network (WSN), and it has a great influence on confidentiality, integrity, and availability. However, many existing IDS only detect single attack or multiple known attacks. In this paper, a novel intrusion detection algorithm based on change rates of multiple attributes (CRMA) is proposed, which can detect multiple attacks including known and unknown types simultaneously. The change rates of multiple attributes for sensor nodes usually reflect the running states of WSN over a period of time. First, the Observed Change Rate of attributes at different times is obtained by observing multiple attributes of different sensor nodes. Then, the convex optimization is alternately used to obtain the Normal Change Rate and corresponding weights by minimizing the distance between the Observed Change Rate and the Normal Change Rate of each attribute. Finally, the WSN is considered to be attacked when the weighted deviation of the Observed Change Rate and Normal Change Rate is beyond the corresponding threshold. Experimental results show that the CRMA can detect multiple attacks including known and unknown types simultaneously and has a fast convergence rate. The average true positive rates (TPR) of CRMA are high, and the average false positive rates (FPR) of CRMA are low. The detection performance of CRMA is superior to that of the ARMA and NeTMids algorithms.

The source localization using direction of arrival (DOA) of target is an important research in the field of Internet of Things (IoTs). However, correntropy suffers the performance degradation for direction of arrival when the two signals contain the similar impulsive noise, which cannot be detected by the difference between two signals. This paper proposes a new correntropy, called the median-difference correntropy, which combines the generalized correntropy and the median difference. The median difference is defined as the deviation between the sampling value and the median of the signal, and it intuitively reflects the abnormality of impulsive noise. Then, the median difference is combined with the generalized correntropy to form a new weighting factor that can effectively suppress the amplitude level of impulsive noise. To improve the robustness of the algorithm, an adaptive kernel size is also integrated into the weighting factor to obtain the optimal local feature. The influence of adaptive kernel sizes on the proposed algorithm is simulated, and the comparison between three typical direction-of-arrival estimation algorithms is conducted. The results show that the accuracy of the median-difference correntropy is significantly superior to the correntropy-based correlation and the phased fractional lower-order moment for a wide range of alpha-stable distribution noise environments.

Industrial wireless sensor network design requires efficient channel usages and timeslot assignment. In this paper, we describe an integrated channel-timeslot allocation algorithm based on a routing-tree coloring scheme. According to a strict routing-tree definition and corresponding resource allocation principles, the algorithm performs in two phases. In the first phase, a traditional mesh sensor network is mapped to a routing tree and each node is colored algorithmically. In the second phase, timeslots are assigned on the colored routing tree according to principles of timeslot allocation. The total number of timeslots necessary by this algorithm for a generic sensor network is theoretically analyzed and the algorithm performance is also evaluated by simulations. The algorithm is compatible with all three latest industrial wireless network standards, WIA-PA, WirelessHART, and ISA 100.11a.

Wireless sensor networks provide an opportunity to enhance the current equipment diagnosis systems in the process industry, which have been based so far on wired networks. In this paper, we use our experience in the Anshan Iron and Steel Factory, China, as an example to present the issues from the real field of process industry, and our solutions. The challenges are three fold: First, very high reliability is required; second, energy consumption is constrained; and third, the environment is very challenging and constrained. To address these issues, it is necessary to put systematic efforts on network topology and node placement, network protocols, embedded software, and hardware. In this paper, we propose two technologies i.e. design for reliability and energy efficiency (DRE), and design for reconfiguration (DRC). Using these techniques we developed Anshan, a wireless sensor network for monitoring the temperature of rollers in a continuously annealing line and detecting equipment failures. Project Anshan includes 406 sensor nodes and has been running for four months continuously.