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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Back-propagation Artificial Neural Network for Early Diabetic Retinopathy Detection Based On A Priori Knowledge

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Journal of Physics: Conference Series 2020年第1期1437卷

作者： Huiqun Wu Shenghui Zhao Xiaofeng Zhang Aimin Sang Jiancheng Dong Kui Jiang Department of Medical Informatics Medical School of Nantong University Nantong China Nantong University The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions (Wuyi University) Nantong China Department of Ophthalmology Affiliated Hospital of Nantong University Nantong China

Purpose: We aim to develop a back-propagation artificial neural network (BP-ANN) improved by a priori knowledge and to compare its efficacy with other methods in early diabetic retinopathy (DR) detection. Methods: A total of 240 fundus images, composed of 120 early-stage DR and 120 normal images, were obtained with the same 45° field of view camera, with the macula at the center, as a cohort for further training. All retinal images were processed, and a priori knowledge features such as blood vessel width and tortuosity were semi-automatically extracted. An improved BP-ANN with a priori knowledge was developed, and its efficacy was compared with that of the traditional BP network and SVM. Besides, k-fold cross validation method was conducted to demonstrate the efficiency of the proposed methods. We also developed a graphical user interface of our proposed BP-ANN to aid in DR screening. Results: Our 10 randomization and 5-fold cross validation results of SVM, traditional BP, and improved BP were compared. The results indicated that the BP-ANN with a priori knowledge can achieve better detection results. Besides, our results were also comparable with other reported state-of-art algorithms. During the training stage, the epoch in the improved BP-ANN was less than that in the traditional BP group (109 vs 254), indicating that the time cost was shorter when using our improved BP-ANN. Furthermore, the accuracy and epoch of both the traditional BP and our improved BP network obtained better performances when the number of hidden neurons was 20. Conclusions: A priori knowledge-based BP-ANN could be a promising measure for early DR detection. CCS: information system→Expert system

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Direct clustering algorithm based on generalized information distance

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2007年第4期44卷 674-679页

作者： Ding, Shifei Shi, Zhongzhi Jin, Fengxiang Xia, Shixiong College of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China College of Geoinformation Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

In this paper a novel direct clustering algorithm based on generalized information distance (GID) is put forward. Firstly, based on information theory, a basic concept of measure of diversity is given and an inequality about measure of diversity is proved. Based on this inequality, a concept of increment of diversity is discussed and a defined. Secondly, by analyzing distance measure, two new concepts of generalized information distance (GID) and improved generalized information distance (IGID) are proposed, and a new direct clustering algorithm based on GID and IGID is designed. Finally this algorithm is applied to soil fertility data processing, and compared with hierarchical clustering algorithm (HCA). The results of simulation application show that the algorithm presented here is feasible and effective. Because of simplicity of algorithm and robustness. It provides a new research approach for studies of pattern recognition theory.

关键词： Clustering algorithms

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Large Generative Model Assisted 3D Semantic Communication

arXiv

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arXiv 2024年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction;2) Latent semantic redundancy;and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Fast stochastic ordinal embedding with variance reduction and adaptive step size

arXiv

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Remote sensing identification of shrub encroachment in grassland in Inner Mongolia

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Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2014年第11期30卷 152-158页

作者： Dong, Zhou Zhao, Xia Liang, Dong Huang, Wenjiang Peng, Dailiang Huang, Linsheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China School of Electronic and Information Engineering Anhui University Hefei 230039 China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100094 China State Key Laboratory of Vegetation and Environmental Change Institute of Botany Chinese Academy of Sciences Beijing 100093 China

Shrub encroachment has been a wide phenomenon across the arid and semi-arid grasslands in Inner Mongolia, China. Although numerous studies have investigated the effect of this phenomenon on community composition, ecosystem structure, and nutrient cycling, reports on the distribution patterns of shrub encroachment are limited. A recent development in satellite remote sensing enables accurate assessment of shrub distribution and its dynamics at large scales. In this paper, the combined ground survey in Xianghuangqi, four satellite images (with spatial resolution of 5.8 m) of ZY-3, covering nearly the whole area and taken between July and August in 2013, were used to identify the shrub distribution in this region. It should be noted that the shrub here indicated the shrub-grass mosaic due to the mixed pixel effect, and the identification was weak when the coverage of shrub was on low levels. The NDVI threshold method was first used to extract the vegetation coverage area, and then three traditional pixel-oriented methods (Support vector machine, Maximum likelihood and Mahalanobis distance), compared with the object-oriented method, were used for the classification of images. Object-oriented method is different from the traditional one, in that the classification is not based on the spectral characteristics of individual pixel, but relies on the image object with spatial texture and shape and size characteristics. Ground survey data were used to compare the accuracy level of these methods. It indicated that the shrub recognition accuracy by using support vector machine algorithm is the highest among the three pixel-oriented methods, with higher producer accuracy and user accuracy than the other two algorithms. Furthermore, the overall classification accuracy of this algorithm is 81.15% higher than that of the maximum likelihood (73.33%) and the Mahalanobis distance (61.77%). However, the overall recognition accuracy by using the object-oriented approach (combined scale

关键词： Maximum likelihood

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

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arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile Edge computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network

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Scientific reports 2019年第1期9卷 2474页

作者： Dong-Ling Yu Yuan-Lin Ma Zu-Guo Yu Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 P.R. China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan Hunan 411105 P.R. China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology Brisbane Q4001 Australia. yuzuguo@***.

More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://***/myl446/BRWHNHA .

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junyi Bian Rongze Jiang Weiqi Zhai Tianyang Huang Xiaodi Huang Hong Zhou Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China School of Computing Mathematics and Engineering Charles Sturt University New South Wales Australia Atypon Systems LLC UK Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Zhangjiang Fudan International Innovation Center Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available 1 .

关键词： Training Dictionaries Codes Semantics Named entity recognition Complexity theory Labeling Data mining Bioinformatics

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