通讯机构：
[Liu, GH ] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.
关键词：
Spintronics;Spin polarization;Antiferromagnetic;MnBi2Te4 film;Electronic transport
摘要：
The spin polarization of current plays an important role in the performance of spintronic devices. Therefore, a highly spin -polarized current source has always been explored through various methods. We study the effects of magnetic order on the electronic structures of antiferromagnetic (AFM) MnBi 2 Te 4 films. A significant spin splitting is found in the surface states of a AFM MnBi 2 Te 4 film with three septuple layers (SLs). The AFM film can show typical metallic behavior for spin -down electrons, and exhibit a semiconductor or insulator behavior with a band gap at the Fermi level for spin -up electrons, just like semimetal ferromagnets with theoretical spin polarization up to 100 %. We also study that the electron transport in the 3-SLs AFM film with a square potential barrier, we find a highly spin -polarized current can be switched on and off by modulating the barrier height in the film.
摘要：
Road crack detection in complex scenarios is challenged by vehicles, traffic facilities, road printed signs and fine cracks. In order to better solve these problems, a novel dense nested depth U-shaped structure for crack image segmentation network named DUCTNet is proposed. Firstly, a depth dense nested structure is designed by combining the superior performance of the Unet $++$ dense nested structure and the deep nested structure of U2Net. This structure improves the ability of the model to extract crack features in depth. Second, a novel deep competitive fusion feature extraction block is proposed. It improves the feature dissimilarity between the cracks and the background by competitive fusion. Then, a novel high-density feature fusion attention mechanism is proposed. This method enhances the contextual and sensitive information of cracks both horizontally and vertically by increasing the feature density. Finally, DUCTNet achieves the best results in comparison tests with eight state-of-the-art specialized crack segmentation networks in both self-built datasets and four public datasets. In addition, DUCTNet achieves excellent results in real road tests, which proves that DUCTNet can provide engineers and technicians with a better means of detecting road cracks.
关键词：
deep learning (DL);localization;segmentation;subcutaneous adipose tissue (SAT);visceral adipose tissue (VAT)
摘要：
The distribution of adipose tissue in the lungs is intricately linked to a variety of lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Accurate detection and quantitative analysis of subcutaneous and visceral adipose tissue surrounding the lungs are essential for effectively diagnosing and managing these diseases. However, there remains a noticeable scarcity of studies focusing on adipose tissue within the lungs on a global scale. Thus, this paper introduces a ConvBiGRU model for localizing lung slices and a multi-module UNet-based model for segmenting subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), contributing to the analysis of lung adipose tissue and the auxiliary diagnosis of lung diseases. In this study, we propose a bidirectional gated recurrent unit (BiGRU) structure for precise lung slice localization and a modified multi-module UNet model for accurate SAT and VAT segmentations, incorporating an additive weight penalty term for model refinement. For segmentation, we integrate attention, competition, and multi-resolution mechanisms within the UNet architecture to optimize performance and conduct a comparative analysis of its impact on SAT and VAT. The proposed model achieves satisfactory results across multiple performance metrics, including the Dice Score (92.0% for SAT and 82.7% for VAT), F1 Score (82.2% for SAT and 78.8% for VAT), Precision (96.7% for SAT and 78.9% for VAT), and Recall (75.8% for SAT and 79.1% for VAT). Overall, the proposed localization and segmentation framework exhibits high accuracy and reliability, validating its potential application in computer-aided diagnosis (CAD) for medical tasks in this domain.
通讯机构：
[Zhong, SH ] C;Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Peoples R China.
关键词：
Age of Information;resource scheduling;Industrial Internet of Things;deep reinforcement learning
摘要：
Effective resource scheduling methods in certain scenarios of Industrial Internet of Things are pivotal. In time-sensitive scenarios, Age of Information is a critical indicator for measuring the freshness of data. This paper considers a densely deployed time-sensitive Industrial Internet of Things scenario. The industrial wireless device transmits data packets to the base station with limited channel resources under the constraints of Age of Information. It is assumed that each device has the capacity to store the packets it generates. The device will discard the data to alleviate the data queue backlog when the Age of Information of the data packet exceeds the threshold. We developed a new system utility equation to represent the scheduling problem and the problem is expressed as a trade-off between minimizing the average Age of Information and maximizing network throughput. Inspired by the success of reinforcement learning in decision-processing problems, we attempt to obtain an optimal scheduling strategy via deep reinforcement learning. In addition, a reward function is constructed to enable the agent to achieve improved convergence results. Compared with the baseline, our proposed algorithm can achieve better system utility and lower Age of Information violation rate.
摘要：
Password, fingerprint, and face recognition are the most popular authentication schemes on smartphones. However, these user authentication schemes are threatened by shoulder surfing attacks and spoof attacks. In response to these challenges, eye movements have been utilized to secure user authentication since their concealment and dynamics can reduce the risk of suffering those attacks. However, existing approaches based on eye movements often rely on additional hardware (such as high-resolution eye trackers) or involve a time-consuming authentication process, limiting their practicality for smartphones. This article presents DEyeAuth, a novel dual-authentication system that overcomes these limitations by integrating eyelid patterns with eye gestures for secure and convenient user authentication on smartphones. DEyeAuth first leverages the unique characteristics of eyelid patterns extracted from the upper eyelid margins or creases to distinguish different users and then utilizes four eye gestures (i.e., looking up, down, left, and right) whose dynamism and randomness can counter threats from image and video spoofing to enhance system security. To the best of our knowledge, we are among the first to discover and prove that the upper eyelid margins and creases can be used as potential biometrics for user authentication. We have implemented the prototype of DEyeAuth on Android platforms and comprehensively evaluated its performance by recruiting 50 volunteers. The experimental results indicate that DEyeAuth achieves a high authentication accuracy of 99.38% with a relatively short authentication time of 6.2 s and is effective in resisting image presentation, video replaying, and mimic attacks.
摘要：
Camellia oleifera, a woody oil tree, is widely recognized for its valuable oil. Different cultivars of C.oleifera exhibit distinct growth characteristics, oil content, and oil composition. Therefore, the classification of C.oleifera cultivars can aid in the better utilization of C.oleifera resources and improve yield and quality. However, the identification of cultivars remains challenging due to genetic diversity, similarities in leaf morphology, and the influence of geographical environment, among other factors. Comprehensive cultivar identification methods for studying C.oleifera must be established to overcome these obstacles. We selected 118 varieties that grew under natural light conditions and collected whole pest-free mature leaves. After filtering out invalid images, we constructed a leaf cultivar dataset consisting of 30890 images of C. oleifera. The results showed that RegNetY-4.0GF-Convolutional Block Attention Module provides significant advantages over other methods in cultivar recognition, including VGG16, ResNet50, EffificientNet-B4, and EffificientNet-B4-CBAM. It achieved an overall accuracy of 93.7 % and an F1-score of 0.945, much higher than the accuracy of other compared methods. CBAM can significantly improve the accuracy of varieties recognized. The overall results showed that deep learning could effectively distinguish C.oleiera leaves of different varieties. This method provided an effective way to identify C.oleifera varieties quickly and nondestructively.
摘要：
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p><jats:italic>Camellia oleifera</jats:italic>, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of <jats:italic>C.oleifera</jats:italic>, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation <jats:italic>R</jats:italic><jats:sup>2</jats:sup> and and time consumption.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum <jats:italic>R</jats:italic><jats:sup>2</jats:sup> improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of <jats:italic>C. oleifera</jats:italic>, providing a valuable tool for predicting the impact of grafting combinations on the fruit of <jats:italic>C. oleifera</jats:italic>.</jats:p>
</jats:sec>
作者机构：
[Zhu, Wenke; Zhao, Yunjing] Cent South Univ Forestry & Technol, Coll Bangor, Changsha 410004, Hunan, Peoples R China.;[Dai, Weisi; Zhou, Guoxiong; Liu, Zewei] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Tang, Chunling] Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Zhou, GX ; Tang, CL ] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China.;Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Hunan, Peoples R China.
关键词：
Deep learning;Informer;Stock price prediction;Time series forecasting model
摘要：
Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model's efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.
期刊：
Computer-Aided Civil and Infrastructure Engineering,2024年39(4):617-634 ISSN：1093-9687
通讯作者：
Zhou, GX
作者机构：
[Yang, Yixin; Sun, Lixiang; Zhou, Guoxiong; Chen, Aibin; Zhang, Yukai] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.;[Cai, Weiwei] Jiangnan Univ, Coll Artificial Intelligence & Comp Sci, Wuxi, Peoples R China.;[Li, Liujun] Univ Idaho, Dept Soil & Water Syst, Moscow, ID USA.
通讯机构：
[Zhou, GX ] C;Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Peoples R China.
摘要：
<jats:title>Abstract</jats:title><jats:p>The segmentation accuracy of bridge crack images is influenced by high‐frequency light, complex scenes, and tiny cracks. Therefore, an integration–competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale‐oriented adjustment algorithm is proposed to solve the high‐frequency light problem. Second, an integration–competition mechanism is proposed to detach complex backgrounds and grayscale features of cracks. Finally, a tiny attention mechanism is proposed to extract the shallow features of tiny cracks. CCSNet outperforms seven state‐of‐the‐art crack segmentation methods in both generalization and comparison experiments on self‐built dataset and four public datasets. It also achieved excellent performance in practical bridge crack tests. Therefore, CCSNet is an effective auxiliary method for lowering the cost of bridge safety detection.</jats:p>
摘要：
Deep hashing cross-modal image-text retrieval has the advantage of low storage cost and high retrieval efficiency by mapping different modal data into a Hamming space. However, the existing unsupervised deep hashing methods generally relied on the intrinsic similarity information of each modal for structural matching, failing to fully consider the heterogeneous characteristics and semantic gaps of different modalities, which results in the loss of latent semantic correlation and co-occurrence information between the different modalities. To address this problem, this paper proposes an unsupervised deep hashing with multiple similarity preservation (UMSP) method for cross-modal image-text retrieval. First, to enhance the representation ability of the deep features of each modality, a modality-specific image-text feature extraction module is designed. Specifically, the image network with parallel structure and text network are constructed with the vision-language pre-training image encoder and multi-layer perceptron to capture the deep semantic information of each modality and learn a common hash code representation space. Then, to bridge the heterogeneous gap and improve the discriminability of hash codes, a multiple similarity preservation module is builded based on three perspectives: joint modal space, cross-modal hash space and image modal space, which aids the network to preserve the semantic similarity of modalities. Experimental results on three benchmark datasets (Wikipedia, MIRFlickr-25K and NUS-WIDE) show that UMSP outperforms other unsupervised methods for cross-modal image-text retrieval.
摘要：
Rice is a crucial agricultural crop, yet it frequently suffers from various diseases, leading to decreased yields and, in severe cases, crop failure. Diseases significantly affect rice growth and yield, resulting in economic losses and food security challenges. The role of image recognition in identifying rice diseases is critical in agricultural production. It enables automated and efficient detection of rice diseases, which is essential for effective management, ensuring food security and sustainable agriculture. To address issues like background noise and edge blurring in rice disease image capture, as well as challenges in determining the optimal learning rate during the training of traditional rice disease recognition networks, a novel method based on PSOC-DRCNet is proposed for rice disease recognition.. First, tto solve the problem of background interference, Dual Mode Attention (DMA) is proposed to adaptively capture meaningful regions in rice disease images. Second, the Residual Adaptive Block (RAB) is proposed, which utilizes dimensional changes and channel attention to solve edge blur problems. Then, a Cross entropy and regularized mixed Loss function (CerLoss), is proposed to optimize the learning strategy of the model in the process of processing datasets and enhance the performance and generalization ability of the model to avoid overfitting problems. Ultimately, In response to the cumbersome problem of finding the optimal learning rate, we propose using Particle Swarm Optimization Chameleon (PSOC) to find the optimal learning rate and train the PSOC-DRCNet model on our custom dataset and compare it with other existing methods and the final average classification accuracy of PSOC-DRCNet is 93.88% with an F1 score of 0.940. We compare it with other existing methods. It is proved that the average classification accuracy of our model under hyper-parameter unification is 92.65% F1 score is 0.928. We validated the PSOC-DRCNet by conducting comparative analyses with other models and through generalization experiments and module effectiveness tests. Additionally, the practicality of PSOC-DRCNet was confirmed through its application in real-world scenarios. The methods proposed in this paper successfully enable the identification of various diseases in rice leaves, offering a practical solution for incorporating deep learning into the agricultural production process. Furthermore, these findings serve as a valuable reference for disease identification in other crops.
摘要：
Unmanned aerial vehicle (UAV) edge computing systems provide easy-to-deploy and low-cost services at those areas with inadequate infrastructure by deploying UAVs as moving edge servers for large-scale users. However, user devices are generally distributed unevenly in a large area, which makes it difficult for existing efforts to cope with this realistic scenario for optimal deployment of UAVs. Therefore, this paper considers a multiple UAV (Multi-UAV) Collaborative edge Computing (UCC) system by utilizing collaboration among them to split computation tasks at UAVs to balance the load and improve resource utilization. In order to maximize the energy-efficiency of the UCC system under the satisfaction of the delay constraint, we study the joint problem of UAV deployment, task collaborative offloading, computation and communication resource allocation in UCC system. We propose a bi-level optimization framework to solve the formulated non-convex mixed-integer optimization problem. In the upper level, the UAV deployment is optimized based on an improved differential evolution (DE) algorithm, and in the lower level the offloading decision and resource allocation are optimized based on a Reinforcement Learning (RL) algorithm with Twin Delayed Deep Deterministic policy gradient. Experimental results demonstrate the effectiveness and superiority of multi-UAV collaborative computing, with the proposed framework achieving a 32.4% reduction in energy consumption and an average 30% increase in task completion rate compared to DDPG, ToDeTaS, and other benchmark schemes.
作者机构：
[Ai, Wei; Meng, Tao; Meng, T; Xu, Jia; Shao, Hongen] Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410082, Hunan, Peoples R China.;[Li, Keqin] State Univ New York New Paltz, Dept Comp Sci, New York, NY 12561 USA.
通讯机构：
[Meng, T ] C;Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410082, Hunan, Peoples R China.
摘要：
Entity event deduplication is the task of identifying all duplication entity events that have described the same entity within a set of events. However, the traditional entity event deduplication method has two challenges. First, the traditional method usually used global comparison when finding the duplication entity event, are all entity events in the dataset need to be compared, leading to low performance. Second, when the entity event evolves, the traditional method does not identify it well and reduces the effectiveness. To address these two problems and improve the performance and effectiveness, we propose a two-stage deduplication method based on graph node selection and optimization (TS-NSNO) strategy. In the first stage (TS-NS), we propose a graph node selection strategy, which transforms the global comparison into a local comparison by selecting the leader node, greatly reduces the number of calculations and improves the performance. In the second stage (TS-NO), we propose a graph node optimization strategy, by combining the spatiotemporal distance and entity event importance change of the event evolution, which optimizes the entity event with incorrect judgment to improve the effectiveness. We conduct extensive experiments on real entity event datasets of different sizes, and the results show that our method performs better in terms of performance and effectiveness.
期刊：
IEEE Transactions on Knowledge and Data Engineering,2024年36(11):5482-5494 ISSN：1041-4347
通讯作者：
Meng, T
作者机构：
[Ai, Wei; Meng, Tao; Meng, T; Xie, Canhao; Du, Jayi] Cent South Univ Forestry & Technol, Coll Comp & Math, Changsha 410004, Hunan, Peoples R China.;[Li, Keqin] SUNY Coll New Paltz, Dept Comp Sci, New York, NY 12561 USA.
通讯机构：
[Meng, T ] C;Cent South Univ Forestry & Technol, Coll Comp & Math, Changsha 410004, Hunan, Peoples R China.
摘要：
Community Search (CS) aims to enable online and personalized discovery of communities. Recently, attention to the CS problem in directed graphs (di-graph) needs to be improved despite the extensive study conducted on undirected graphs. Nevertheless, the existing studies are plagued by several shortcomings, e.g., Achieving high-performance CS while ensuring the retrieved community is cohesive is challenging. This paper uses the D-truss model to address the limitations of investigating the CS problem in large di-graphs. We aim to implement millisecond-level D-truss CS in di-graphs by building a summarized graph index. To capture the interconnectedness of edges within D-truss communities, we propose an innovative equivalence relation known as D-truss-equivalence, which allows us to divide the edges in a di-graph into a sequence of super nodes (s-nodes). These s-nodes form the D-truss-equivalence-based index, DEBI, an index structure that preserves the truss properties and ensures efficient space utilization. Using DEBI, CS can be performed without time-consuming access to the original graph. The experiments indicate that our method can achieve millisecond-level D-truss community query while ensuring high community quality. In addition, dynamic maintenance of indexes can also be achieved at a lower cost.
