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arXiv 2024年

作者： Wang, GuangZhen Li, Xiaolei Liang, Nan Key Laboratory of Information and Computing Science Guizhou Province School of Cyber Science and Technology Guizhou Normal University Guizhou Guiyang550025 China College of Physics Hebei Normal University Heibei Shijiazhuang050024 China Joint Center for FAST Sciences Guizhou Normal University Node Guizhou Guiyang550025 China

In this work, we investigate the phenomenologically emergent dark energy (PEDE) model and its generalized form, namely the generalized emergent dark energy (GEDE) model, which introduces a free parameter ∆ that can discriminate between the ΛCDM model and the PEDE model. Fitting the emergent dark energy (EDE) models with the observational datasets including the cosmology-independent gamma-ray bursts (GRBs) and the observational Hubble data (OHD) at intermediate redshift, we find a large value of H0 which is close to the results of local measurement of H0 from the SH0ES Collaboration in both EDE models. In order to refine our analysis and tighten the constraints on cosmological parameters, we combine mid-redshift observations GRBs and OHD with baryon acoustic oscillations (BAOs). Finally, we constrain DE models by using the simultaneous fitting method, in which the parameters of DE models and the relation parameters of GRBs are fitted simultaneously. Our results suggest that PEDE and GEDE models can be possible alternative to the standard cosmological model, pending further theoretical explorations and observational verifications. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark energy

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MSONoC: A Metasurface-assisted Hybrid Waveguide/FSO Network-on-Chip Architecture

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Sun, Wei Li, Jingsi Guo, Pengxing He, Xiangyu Wang, Zimo Hu, Cong Liu, Weichen Hou, Weigang Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communications and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Engineering Nanyang Technological University Singapore

Optical networks-on-chips (ONoCs) have emerged as the most promising solution for on-chip interconnection networks, offering distinct advantages such as high bandwidth and low latency. However, traditional wired-based ONoCs often suffer from a high average number of hops, leading to challenges such as increased insertion loss, crosstalk noise, and energy consumption. Therefore, this paper presents a novel ONoC structure known as the metasurface ONoC (MSONoC), which integrates wired waveguide and free-space optical (FSO) transmission with the assistance of metasurfaces. Specifically, this work encompasses the design of a passive metasurface structure tailored for ONoCs, a virtual network cluster partitioning method, an optical metasurface access points select strategy, and a shortest-path routing algorithm. Performance verification is conducted using a modified Noxim simulator. The simulation results demonstrate that the proposed MSONoC offers advantages over traditional NoCs, radio frequency (RF)-based hybrid wired/wireless NoCs, and wired-based ONoCs in terms of throughput, delay, energy consumption, and signal-to-noise ratio under both synthetic traffic and real traffic models. These improvements proves the effectiveness of the MSONoC architecture in optimizing network performance, making it a promising solution for high-performance, energy-efficient communication in on-chip networks. © 2017 IEEE.

关键词： Network-on-chip

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CODH++: Macro-semantic differences oriented instance segmentation network

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Expert Systems with Applications 2022年 202卷 1页

作者： Zhang, Wenchao Fu, Chong Cao, Lin Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Computer Science The University of Auckland New Zealand

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available. © 2022 Elsevier Ltd

关键词： Semantics

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Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

arXiv

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arXiv 2025年

作者： Yao, Ni Liu, Xiangyu Sun, Danyang Han, Chuang Li, Yanting Nan, Jiaofen Li, Chengyang Zhu, Fubao Zhou, Weihua Zhao, Chen School of Computer Science and Technology Zhengzhou University of Light Industry Henan Zhengzhou450001 China School of Information Management and Engineering Shanghai University of Finance and Economics China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States Department of Computer Science Kennesaw State University MariettaGA United States

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://***/chenzhao2023/MGFA_CCTA. © 2025, CC BY.

关键词： Image segmentation

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Improved Diversity-Promoting Collaborative Metric Learning for Recommendation

arXiv

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arXiv 2024年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Security Department of Alibaba Group Hangzhou311121 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to introduce a set of multiple representations for each user in the system where users' preference toward an item is aggregated by taking the minimum item-user distance among their embedding set. Specifically, we instantiate two effective assignment strategies to explore a proper quantity of vectors for each user. Meanwhile, a Diversity Control Regularization Scheme (DCRS) is developed to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could induce a smaller generalization error than traditional CML. Furthermore, we notice that CML-based approaches usually require negative sampling to reduce the heavy computational burden caused by the pairwise objective therein. In this paper, we reveal the fundamental limitation of the widely adopted hard-aware sampling from the One-Way Partial AUC (OPAUC) perspective and then develop an effective sampling alternative for the CML-based paradigm. Finally, comprehensive experiments over a range of benchmark datasets speak to the efficacy of DPCML. Code are available at https://***/statusrank/LibCML. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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MEDIDRONE—A Predictive Analytics-Based Smart Healthcare System 6th

MEDIDRONE—A Predictive Analytics-Based Smart Healthcare Sys...

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6th International Conference on Big Data and Cloud computing Challenges, ICBCC 2019

作者： Sangeetha, D. Rathnam, M. Venkata Vignesh, R. Chaitanya, Jitta Sai Vaidehi, V. Department of Information Technology Anna University Madras Institute of Technology Campus Chennai India School of Computing Science and Engineering VIT Chennai India

ISBN: (纸本)9789813298880

Health care is one of the constitutional rights of the people in India. Every resident of this country has the right to the highest attainable standard of health both physically and mentally. But, in India, the number of active Primary Health care Centers (PHCs) is very less in number, especially in the remote regions. Even in places where there are sufficient PHCs, there is a shortage of doctors, staffs, and medicine. Proper medication should be offered to people in rural areas in order to improve their health, thereby reducing the number of deaths. The trending technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Predictive Analytics have a key role in improving the medication and in providing preventive medical care. Therefore, exploiting these technologies to diagnose and monitor patients remotely using Body Area Network (BAN) devices and smartphones, the MEDIDRONE is proposed. The MEDIDRONE has been designed to provide on-time emergency services to the people in rural villages using drones. In addition, predictive models have been trained to provide people with insights from the data collected for their long-term welfare. The MEDIDRONE besides providing health insights and alerts to the people, also addresses the issue of unemployment in rural areas. The MEDIDRONE intends to provide the task of periodically collecting the vital signs of the people in the village as a paid work to medical practitioners, social workers, and unemployed people which could improve the economic status of the country. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Drones

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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Towards Securing ASCON Implementation by Inner Product Masking

Towards Securing ASCON Implementation by Inner Product Maski...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yuming Liu Wei Cheng Jihao Fan Yongbin Zhou School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China LTCI Télécom Paris IP Paris and Secure-IC S.A.S Paris France Laboratory for Advanced Computing and Intelligence Engineering Wuxi China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Ascon algorithm has been selected by NIST for standardization of the lightweight cryptography, that will be used in embedded systems, IoT devices, and other resource-constrained devices. As a lightweight cryptographic algorithm, Ascon implementation requires less computation and memory. However, the current implementations of Ascon algorithms and existing protection schemes are vulnerable to side-channel attacks. In this paper, we propose an IPM (inner product masking) based protection for Ascon implementations. Our new masking scheme can prevent side-channel attacks from successfully retrieving the key, even when the leakage levels are nearly identical. Alternatively, the cost of obtaining the key can increase by several orders of magnitude, making the attack significantly more challenging. The proposed scheme is experimentally validated by porting it into a STM32F407 microcontroller and conducting correlation power analysis (CPA) and template attack (TA) on the collected power traces. The experimental results show that while CPA can successfully retrieve the keys from both the unprotected Ascon implementation and the Boolean-masked Ascon implementation, they fail against the new masking scheme. Although TA attacks can break the new scheme, they require approximately 50 times more data compared to attacking the Boolean mask. This demonstrates that the new masking scheme offers significantly greater security than both the Boolean mask and the unprotected implementations.

关键词： Resistance Privacy Microcontrollers Refining Memory management Side-channel attacks Standardization NIST Internet of Things Protection

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Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第4期2卷 100088-100088页

作者： Islam, Md. Milon Islam, Md. Zabirul Asraf, Amanullah Al-Rakhami, Mabrook S. Ding, Weiping Sodhro, Ali Hassan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Information Science and Technology Nantong University Nantong226019 China Department of Computer Science Kristianstad University KristianstadSE-29188 Sweden Shenzhen Institutes of Advanced technology Chinese Academy of Sciences Shenzhen China

Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. All the data used during the study are openly available from the Mendeley data repository at https://***/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://***/Asraf047/COVID19-CNN-RNN. © 2023 The Authors

关键词： COVID-19

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Few-Shot Knowledge Graph Completion based on Data Enhancement

Few-Shot Knowledge Graph Completion based on Data Enhancemen...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zepeng Li Peilun Geng Shuo Cao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China Chines Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Shanghai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Knowledge graphs (KGs) are widely used in various natural language processing applications. In order to expand the coverage of a KG, KG completion has attracted extensive attention. The commonly used embedding methods based on a large amount of training data can play an important role in this work. However, with few of triples, the performance of these methods will be greatly reduced. The completion of this kind of few-shot task is more challenging. In this work, we propose a method of data enhancement to increase the data quantity and solve the problem of sample shortage. Specifically, we first observe that the representation vectors of the relation in a KG are approximately subordinate to Gaussian distribution. Then we construct a Gaussian distribution for the relation of each triple in few-shot task according to the distributions of its similar relations in background graph. Further, we sample from the Gaussian distribution of each triple to expand the training data. Finally, we use an adaptive attentional network model FAAN proposed by Sheng et al. as the baseline model. Experimental results on two public datasets NELL-One and Wiki-One show that the proposed method achieves better performance.

关键词： Adaptation models Adaptive systems Biological system modeling Training data Gaussian distribution Natural language processing Task analysis

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