作者机构：
[Guo Sheng; Kang She; Zhengping Shan; Piaorong Xu; Lin Li; Exian Liu] College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
摘要：
Owing to the flexible light control and the immunity to defects and impurities, topological photonic crystals (TPC) are considered an excellent platform in the engineering of optoelectronic devices. Herein, four TPC refractive index (RI) sensors are theoretically proposed based on the su-Schrieffer-Heeger (SSH) model. Zero-dimensional topological corner states (TCS) and 1-D topological edge states (TES) are demonstrated by the band structure and the Zak phases. Four TPC sensors are based on single topological microcavity (TCS formed by two TES and four TES), trivial corner states (TRCS)-TES coupling, and TCS-TES coupling. The TCS-based TPC sensors exhibit an ultrahigh quality factor (Q) of $\gt 10^{{6}}$ . Moreover, TPC sensor based on TCS-TES coupling remain a topological robustness with >99.4% sensitivity quality of reproduction by artificially introducing eight-type defects. Notably, the design based on TCS-TES coupling presents a sensitivity of 413.76 nm/RIU and a Q value of $2.8\times 10^{{6}}$ in a broad RI range of 1.00–1.50. This work not only provides a new way of 2-D photonic crystals (PC) sensor applications but also offers a guidance for the design of TPC sensors.
关键词：
RDH;reversible data hiding;prediction error;Huffman coding;encrypted images;extended run-length coding
摘要：
To reduce prediction errors and create more room for embedding data, the paper proposes a reversible data hiding (RDH) in encrypted images scheme based on histogram shifting and prediction error block coding. Firstly, the histogram of the prediction error image is shifted according to the signs of prediction errors. Next, the prediction error plane is partitioned into uniformly sized blocks, and these blocks are labelled as three types: an all-zero block, a block containing only one 1, and a block containing more than one 1. These three types of blocks are compressed using labelling, binary tree coding, and Huffman coding, respectively. To better compress the label map, an improved extended run-length coding is proposed. Finally, the image is secured by encryption and the secret data is hidden within it. The experimental results indicate a significant improvement in the embedding rate of the scheme compared to other schemes.
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.
作者：
Li Peng;Wang Wang;Cheng Yang;Wenhui Xiao;Xiangzheng Fu;...
作者机构：
[Li Peng; Wang Wang; Cheng Yang; Wenhui Xiao] College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China;[Yifan Chen] College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China;[Xiangzheng Fu] College of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR China
会议名称：
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间：
03 December 2024
会议地点：
Lisbon, Portugal
会议论文集名称：
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
关键词：
miRNA-drug sensitivity;graph neural network;zero-shot embeddings;large language models
摘要：
MicroRNAs (miRNAs) are a class of non-coding RNA molecules that have been shown to be closely associated with the sensitivity of chemotherapeutic drugs in cancer treatment. Given the high cost and extended duration of traditional biological experiments, there is an urgent need to develop computational models to predict the sensitivity scores between miRNAs and drugs. In this study, we proposed a dual-stream graph neural network method based on Zero-Shot Embeddings, named DSHGZS, to explore the potential sensitivity scores between miRNAs and drugs. DSHGZS first constructs two heterogeneous graphs with different isomorphic subgraphs based on zero-shot embeddings obtained from large language models (LLMs) and known miRNA-drug association data. It then utilized the enhanced LLM-derived node feature representations, embedding them into the layer feature learning process of the two heterogeneous graphs to generate high-quality vector representations of miRNAs and drugs. The learned high-quality feature embeddings are subsequently used in a segmented inner product decoder to evaluate the sensitivity association scores between miRNAs and drugs. To address the model’s excessive reliance on high-quality feature representations, we employed PCA to extract the core representations of the LLM-derived node features for data augmentation. Case studies demonstrated that DSHGZS is an effective tool for predicting potential sensitivity scores between miRNAs and drugs.
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.
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.
会议论文集名称：
2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)
关键词：
Geospatial data aggregation;local differential privacy;Markov matrix;truth discovery
摘要：
Aggregating geospatial data plays a crucial role in location-based services. However, collecting such sensitive data raises concerns about location privacy leakage. Local Differential Privacy (LDP), as a de facto privacy paradigm, has been widely employed to ensure individual location privacy. Nonetheless, existing approaches for aggregating geospatial data under LDP either suffer from compromised accuracy or involve complex computations. In this work, we propose a history-aware geospatial data aggregation framework to enhance both accuracy and efficiency while guaranteeing LDP. To this end, we first investigate an efficient aggregation method, namely General Randomized Response (GRR), and find that its variance of aggregation error follows the sum of two zero-mean binomial distributions. This reveals that multiple aggregations can boost the accuracy of GRR. To obtain multiple aggregations without compromising privacy, we adopt a Markov transition model to complement current aggregations from historical ones. However, learning the Markov transition matrix on perturbed data is challenging. Accordingly, we propose a privacy-aware Markov Transition Matrix Estimation (MTME) algorithm. Finally, we introduce a truth discovery-based refinement algorithm to iteratively derive an accurate aggregated result from multiple inaccurate aggregations. We evaluate our proposed method on two real-world trajectory datasets, and thorough experiments demonstrate its superior accuracy and very low time overhead compared to competitors.
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>
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.
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>
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：
Unmanned Aerial Vehicles (UAVs) are heavily used in disaster or emergency scenarios. In this paper, we investigate the joint problem of task offloading, task scheduling, transmission power and computing resources allocation, and optimization of UAV deployment location for UAV-enabled Mobile Edge Computing (MEC), considering and highlighting the order of execution and transmission of different tasks. The corresponding optimization problem, which is a non-convex mixed-integer optimization problem, is formulated. In oder to solve this problem, the formulated problem is decomposed into three Sub-Problems (SP), and an iterative method based on Block Coordinate Descent (BCD) is proposed. Given the UAV location and resource allocation, the 1-st SP (SP1) of task offloading scheduling optimization is solved by greedy strategy optimization methods. Given the task offloading decision, task scheduling order, and resource allocation, the 2-nd SP (SP2) of optimizing the deployment location of UAV is solved by Successive Convex Approximation (SCA) optimization methods. Given the task offloading decision, task scheduling order, and UAV location, the 3-rd SP (SP3) of transmission and computing resources allocating is solved by convex optimization methods. Simulation results show that our proposed method can significantly reduce energy consumption compared to the benchmark schemes.
摘要：
To address the problems of slow convergence, low search accuracy, and easy fall into local optimum, and generating a large number of infeasible solutions when solving the 0–1 Knapsack Problem, which makes it difficult to obtain the optimal solution scheme, in this paper, we present a greedy induced mutation method for locally optimal solutions, namely, an improved Shuffled Frog Leaping Algorithm (Shuffled Frog Leaping Algorithm based on Greedy Algorithm and Mutation, SFLA-GA-M). First, the proposed algorithm adjusts the population generation of individuals in the SFLA to avoid infeasible solutions, thereby optimizing the local update strategy. Secondly, an induced mutation mechanism that incorporates both greedy algorithms and genetic algorithms is introduced to enhance the search accuracy of the algorithm. Finally, ten classical 0–1 Knapsack Problem cases were selected to prove the feasibility and robustness of the proposed SFLA-GA-M by comparing with other two SFLA variants, which are MDSFLA, DSFLA. Meanwhile, to further verify the performance of the SFLA-GA-M, two classical 0–1 KPs with multi-dimensional test cases were introduced by compared with other five algorithm: DEA, PSO, GA, BMA and IFMA, respectively. The experimental results show that SFLA-GA-M has achieved stronger convergence and stability compared to DEA, PSO, and GA, and it has displayed better search efficiency and a superior global search capability than BMA and IFMA in solving large-sized 0–1 Knapsack Problem.
