Reviewerly/exHarmony huggingface dataset:
Research on Distributed Dynamic Trusted Access Control Based on Security Subsystem

The flow of data across nodes has become the dominant feature of data sharing in distributed environments with increasingly blurred boundaries, where it is crucial to maintain data access dynamic, trusted, and efficient. However, traditional centralized access control models are not only difficult to apply in distributed environments but also ignore trusted verification of authorized entities. What’s worse, existing access control models rarely consider themselves security, lack independence, at a high risk of being bypassed or tampered with. Thus, we propose in this paper a distributed, dynamic, and trusted access control model, DDTAC-BSS, where the standard Attribute-Based Access Control (ABAC) architecture is modified and extended. To reduce the attack surface, we separate policy enforcement point (PEP) from other core components, they are located in the node system and access control system, respectively. Then, the access control entry point (ACEP) is added as the only interface for the node system to interact with the access control system. Subsequently, the model introduces the entity trusted assessment mechanism to improve the trustworthiness of access control services. Driven by the dynamic attributes, our model can achieve dynamic trusted authorization and fine-grained access control. Moreover, we implement a lightweight, independent, and distributed security subsystem to achieve unified management of policies and decision-making autonomy by message-driven. By considering the independence of the security subsystem, a trusted operating environment is built based on Trusted Execution Environment (TEE) to ensure the security of the access control mechanism itself. The security of our model is proved rigorously based on the non-interference theory. Comprehensive experiments and comparisons have demonstrated the superior functionality, comparable performance, and strong security of our model.
Telescoped boiling and cooling mechanisms triggered hydrothermal stibnite precipitation: Insights from the world’s largest antimony deposit in Xikuangshan China

Abstract Society annually consumes 250% more Sb relative to the year 1960 and a sustainable supply of antimony depends critically on understanding the precipitation mechanism of stibnite (Sb2S3) that is the globally predominant source of this important technology metal. Previous solubility studies revealed that antimony is transported in mesothermal hydrothermal fluids mainly as the aqueous species thioantimonite (H2Sb2S4, HSb2S4−, Sb2S42−) and hydroxothioantimonite [Sb2S2(OH)2]. Thioantimonite can transform to hydroxothioantimonite with a decline of H2S concentration. However, whether this transition occurs in hydrothermal systems and its role in stibnite precipitation are unknown. In this work, bulk Sb isotope measurements for stibnite from the world’s largest Sb deposit in Xikuangshan China were conducted to address ore fluid evolution and stibnite precipitation mechanisms. The abundance of the stable antimony isotopes 121Sb and 123Sb were measured in stibnite from the Xikuangshan orebodies and reported as δ123Sb. The δ123Sb values show a trend of decreasing first and then increasing from proximal to distal parts of orebodies. This reveals that 123Sb had been preferentially partitioned from the ore fluid into stibnite first, then 123Sb remained preferentially dissolved in the ore fluid. These data indicate that the dominant Sb-complex transforms to Sb2S2(OH)2 from H2Sb2S4 with consumption of H2S. Speciation diagram considerations indicate that stibnite precipitation from the ore fluid was controlled by two telescoped processes: (1) boiling of the ore fluid induced a decrease in H2S that reduced the solubility of H2Sb2S4, and (2) subsequent cooling that induced a decrease in the solubility of Sb2S2(OH)2. This study highlights that understanding the controls of Sb isotope fractionation is critical to constrain fluid evolution and stibnite precipitation mechanisms in Sb-rich mineral systems. In particular, the stable Sb complex in the hydrothermal ore fluid may change during fluid evolution and affect the isotope fractionation mechanism.
3D Global Fourier Network for Alzheimer’s Disease Diagnosis Using Structural MRI

Deep learning models, such as convolutional neural networks and self-attention mechanisms, have been shown to be effective in computer-aided diagnosis (CAD) of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI). Most of them use spatial convolutional filters to learn local information from the images. In this paper, we propose a 3D Global Fourier Network (GF-Net) to utilize global frequency information that captures long-range dependency in the spatial domain. The GF-Net contains three primary components: a 3D discrete Fourier transform, an element-wise multiplication between frequency domain features and learnable global filters, and a 3D inverse Fourier transform. The GF-Net is trained by a multi-instance learning strategy to identify discriminative features. Extensive experiments on two independent datasets (ADNI and AIBL) demonstrate that our proposed GF-Net outperforms several state-of-the-art methods in terms of accuracy and other metrics, and can also identify pathological regions of AD. The code is released at https://github.com/qbmizsj/GFNet.
Integral representations of shallow neural network with rectified power unit activation function

In this paper we characterize the set of functions that can be represented by infinite width neural networks with RePU activation function max(0,x)p, when the network coefficients are regularized by an ℓ2/p (quasi)norm. Compared to the more well-known ReLU activation function (which corresponds to p=1), the RePU activation functions exhibit a greater degree of smoothness which makes them preferable in several applications. Our main result shows that such representations are possible for a given function if and only if the function is κ-order Lipschitz and its R-norm is finite. This extends earlier work on this topic that has been restricted to the case of the ReLU activation function and coefficient bounds with respect to the ℓ2 norm. Since for q<2, ℓq regularizations are known to promote sparsity, our results also shed light on the ability to obtain sparse neural network representations.
A Memetic Algorithm for Cooperative Complex Task Offloading in Heterogeneous Vehicular Networks

With the booming of intelligent connected vehicles as well as the emergence of edge computing paradigm, complex task offloading becomes a critical yet promising issue in vehicular networks to enable various real-time and scalable future intelligent systems. This article makes the first effort on proposing an end-edge-cloud cooperation architecture together with a tailored memetic algorithm for complex task offloading in heterogeneous vehicular networks. Specifically, we consider the scenario where a complex task consists of multiple subtasks, which require different amount of resources for being processed, and a task is completed only when all of its subtasks have been processed. Then, we formulate a Cooperative Complex Task Offloading (CCTO) problem by considering heterogeneous computation, communication and memory capacities of nodes in vehicular networks, as well as task dependency and mobility of vehicles, targeting at minimizing average service delay of the system. We prove that CCTO is NP-hard by constructing a polynomial time reduction from the parallel machines scheduling problem (PMSP). Further, we propose a memetic computing based algorithm named MDMA (Meme Dependency-aware Memetic Algorithm), which consists of a meme dependency based encoding solution, a mix-strategy for initialization, a dedicated offspring generation scheme, a meme recombined strategy for local search, and a task feature driven method for repairing infeasible solutions. Finally, we build a simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of MDMA on minimizing the service delay by best exploiting heterogeneous resources in vehicular networks.
RGB-Based Triple-Dual-Path Recurrent Network for Underwater Image Dehazing

In this paper, we present a powerful underwater image dehazing technique that exploits two image characteristics—RGB color channels and image features. In using RGB color channels, each color channel is decomposed into two units based on the similarities via the k-mean. This markedly improves the adaptability and identification of similar pixels, and thus reduces pixels with a weak correlation, leaving only pixels with a higher correlation. We use an infinite impulse response (IIR) in the triple-dual and parallel interaction structure to suppress hazed pixels via a pixel comparison and amplification to increase the visibility of even very minor features. This improves the visual perception of the final image, thus improving the overall usefulness and quality of the image. The softmax-weighted fusion is finally used to fuse the output color channel features to attain the final image. This preserves the color, leaving our proposed method’s output very true to the original scene’s. This is accomplished by taking advantage of adaptive learning based on the confidence levels of the pixel contribution variation in each color channel during subsequent fuses. The proposed technique both visually and objectively outperforms the existing methods in several rigorous tests.
Analysis of Deep Learning Methods in Adaptation to the Small Data Problem Solving

This paper discusses a specific problem in the study of deep neural networks - learning on small data. Such issue happens in situation of transfer learning or applying known solutions on new tasks that involves usage of particular small portions of data. Based on previous research, some specific solutions can be applied to various tasks related to machine learning, computer vision, natural language processing, medical data study and many others. These solutions include various methods of general purpose machine and deep learning, being successfully used for these tasks. In order to do so, the paper carefully studies the problems arise in the preparation of data. For benchmark purposes, we also compared "in wild" the methods of machine learning and identified some issues in their practical application, in particular usage of specific hardware. The paper touches some other aspects of machine learning by comparing the similarities and differences of singular value decomposition and deep constrained auto-encoders. In order to test our hypotheses, we carefully studied various deep and machine learning methods on small data. As a result of the study, our paper proposes a set of solutions, which include the selection of appropriate algorithms, data preparation methods, hardware optimized for machine learning, discussion of their practical effectiveness and further improvement of approaches and methods described in the paper. Also, some problems were discussed, which have to be addressed in the following papers.
Support vector machine embedding discriminative dictionary pair learning for pattern classification

Discriminative dictionary learning (DDL) aims to address pattern classification problems via learning dictionaries from training samples. Dictionary pair learning (DPL) based DDL has shown superiority as compared with most existing algorithms which only learn synthesis dictionaries or analysis dictionaries. However, in the original DPL algorithm, the discrimination capability is only promoted via the reconstruction error and the structures of the learned dictionaries, while the discrimination of coding coefficients is not considered in the process of dictionary learning. To address this issue, we propose a new DDL algorithm by introducing an additional discriminative term associated with coding coefficients. Specifically, a support vector machine (SVM) based term is employed to enhance the discrimination of coding coefficients. In this model, a structured dictionary pair and SVM classifiers are jointly learned, and an optimization method is developed to address the formulated optimization problem. A classification scheme based on both the reconstruction error and SVMs is also proposed. Simulation results on several widely used databases demonstrate that the proposed method can achieve competitive performance as compared with some state-of-the-art DDL algorithms.
High-accuracy camera calibration method based on coded concentric ring center extraction

In the field of three-dimensional (3-D) metrology based on fringe projection profilometry (FPP), accurate camera calibration is an essential task and a primary requirement. In order to improve the accuracy of camera calibration, the calibration board or calibration target needs to be manufactured with high accuracy, and the marker points in calibration image require to be positioned with high accuracy. This paper presents an improved camera calibration method by simultaneously optimizing the camera parameters and target geometry. Specifically, a set of regularly distributed target markers with rich coded concentric ring pattern is first displayed on a liquid crystal display (LCD) screen. Then, the sub-pixel edges of all coded bands radial straight lines are automatically located at several positions of the LCD screen. Finally, the sub-pixel edge point set is mapped into parameter space to form a line set, and the intersection of the lines is defined as the center pixel coordinates of each target point to complete the camera calibration. The simulation and experimental results verify that the proposed camera calibration method is feasible and easy to operate, which can essentially eliminate the perspective transformation error to improve the accuracy of camera parameters and target geometry.
A multi-head pseudo nodes based spatial–temporal graph convolutional network for emotion perception from GAIT

In the recent years, the emotion recognition task has attracted a lot of attention in the field of human–computer interaction. Most existing research typically uses aural-visual analysis, which has been an effective approach to capturing emotional features. However, aural-visual signals are difficult to notice, in comparison to other human representations such as gait in remote situations. Recently, human gait can be effectively recognized in more complex backgrounds because of the advancement of Graph Convolutional Networks (GCNs). According to the anatomy of the human body, central torso joints play a key role in GCNs-based human gait recognition systems, instead of the body’s marginal limb joints. As a result, there is a major issue of receptive field imbalance. In this study, we propose a method for perceiving emotions based on the human gait skeleton. We present a multi-head pseudo nodes strategy to alleviate the receptive field imbalance problem and capture the non-local dependencies among different joints. The strategy employs a series of extra nodes that link to all physical human body joints and obtain global information from different feature spaces. The results of the experiments on a public emotion-gait dataset demonstrate that our proposed method outperforms existing skeleton-based methods. Further, to verify the effectiveness of our method, we use publicly available human action recognition datasets. Our results show that our method significantly improves performance in comparison to other baseline methods.
Scalable and Conflict-Free NTT Hardware Accelerator Design: Methodology, Proof, and Implementation

Number theoretic transform (NTT) is useful for the acceleration of polynomial multiplication, which is the main performance bottleneck in the next-generation cryptographic schemes. Different NTT-based cryptographic algorithms have different security settings. The diverse application scenarios introduce different cost-performance tradeoffs and hardware constraints. Motivated by the emerging demand for more versatile NTT hardware accelerators, we propose a new design methodology that can generate area-efficient and high-performance NTT accelerators for any length and modulus of NTT polynomials and single processing element (PE) or PE array with a varying number of layers. The proposed NTT accelerator architecture pivots on a conflict-free memory access pattern for adaptation to different combinations of security and PE array configuration parameters. The proposed memory access pattern is formally proved to be conflict-free for any parametric configurations. The criterion for read-after-write conflict without pipeline stall is also established. Our proposed design methodology can produce NTT accelerators with single PE or multilayer PE array for different polynomial size and modulus, with hardware area and computational efficiency comparable to accelerators customized for a fixed set of parameters. Our proposed methodology produces parameterized accelerator with higher scalability than the existing parameterized accelerator design. On average, the accelerators generated by our proposed method are 71.4% more area-time efficient. Up to 30.7% area-time reduction over the most area-time efficient state-of-the-art scalable NTT accelerator can be achieved for the same security parameters.
Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing

Federated learning (FL), an emerging distributed machine learning (ML) technique, allows massive embedded devices and a server to work together for training a global ML model without collecting user data on a server. Most existing approaches adopt the traditional centralized FL paradigm with a single server: one is the cloud-centric FL paradigm and the other is the edge-centric FL paradigm. The cloud-centric FL paradigm is able to manage a large-scale FL system across massive user devices with high communication cost, whereas the edge-centric FL paradigm is capable of coordinating a small-scale FL system benefiting from the low communication delay over wireless networks. To fully exploit the advantages of both, in this article, we develop a distinctive hierarchical FL framework for the promising mobile-edge cloud computing (MECC) system, called HELCHFL, to achieve high-efficiency and low-cost hierarchical FL training. In particular, we formulate the corresponding theoretical foundation for our HELCHFL to ensure hierarchical training performance. Furthermore, to address the inherent communication and user heterogeneity issues of FL training, our HELCHFL develops a utility-driven and heterogeneity-aware heuristic user selection strategy to enhance training performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCHFL introduces a device operating frequency determination approach to reduce training energy cost. Experiments demonstrate that our HELCHFL can enhance the highest accuracy by up to 52.93%, gain the training speedup of up to 483.74%, and obtain up to 45.59% training energy savings compared to state-of-the-art baselines.
Clustering consistency with Dirichlet process mixtures

Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribution induced by Dirichlet process mixtures as the sample size increases, and more specifically focus on consistency for the unknown number of clusters when the observed data are generated from a finite mixture. Crucially, we consider the situation where a prior is placed on the concentration parameter of the underlying Dirichlet process. Previous findings in the literature suggest that Dirichlet process mixtures are typically not consistent for the number of clusters if the concentration parameter is held fixed and data come from a finite mixture. Here we show that consistency for the number of clusters can be achieved if the concentration parameter is adapted in a fully Bayesian way, as commonly done in practice. Our results are derived for data coming from a class of finite mixtures, with mild assumptions on the prior for the concentration parameter and for a variety of choices of likelihood kernels for the mixture.
A Heuristic Remote Entanglement Distribution Algorithm on Memory-Limited Quantum Paths

Remote entanglement distribution plays a crucial role in large-scale quantum networks, and the key enabler for entanglement distribution is quantum routers (or repeaters) that can extend the entanglement transmission distance. However, the performance of quantum routers is far from perfect yet. Amongst the causes, the limited quantum memories in quantum routers largely affect the rate and efficiency of entanglement distribution. To overcome this challenge, this paper presents a new modeling for the maximization of entanglement distribution rate (EDR) on a memory-limited path, which is then transformed into entanglement generation and swapping sub-problems. We propose a greedy algorithm for short-distance entanglement generation so that the quantum memories can be efficiently used. As for the entanglement swapping sub-problem, we model it using an Entanglement Graph (EG), whose solution is yet found to be at least NP-complete. In light of it, we propose a heuristic algorithm by dividing the original EG into several sub-problems, each of which can be solved using dynamic programming (DP) in polynomial time. By conducting simulations, the results show that our proposed scheme can achieve a high EDR, and the developed algorithm has a polynomial-time upper bound and reasonable average runtime complexity.
An Intelligent Virtual Standard Patient for Medical Students Training Based on Oral Knowledge Graph

Virtual standard patient (VSP) is in high demand for medical students' diagnosis ability training in an efficient manner. Different from the traditional conversation system in medical dialogue generation, VSP needs a novel conversation paradigm to act as the patient instead of the doctor. However, existing conversation techniques still have limited ability in terms of generation of symptoms exhibited by patients with the personalized and knowledge-centered expressions. To alleviate these problems, we propose to construct a novel oral knowledge graph, which sufficiently provides medical clues of the certain disease. Accordingly, the VSP could accurately interact with the dentists for their underlying intention and express the symptoms characters in a natural style. To efficiently retrieve the related disease clues, the symptoms descriptions of the oral diseases are encoded into the oral knowledge graph, which could well organize the disease-centered symptom entities and speaking styles. Moreover, to transfer the common sense knowledge from existing large scale of medical knowledge graph to the specific oral knowledge graph, a coupled pre-trained Bert models is further designed to learn the related medical knowledge from coarse-level to fine-level hierarchically. Finally, a series of well-designed personalized templates are proposed to generate plausible and realistic answers in condition of the certain disease. We also conduct extensive user studies to demonstrate that the VSP satisfies the medical students' diagnosis practice requirement in terms of naturalness, realism, and topic relevance.