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SSRN 2024年

作者： Chen, Xi Xu, Xiaoyu Tong, Lyuyang Zhao, Huangxuan Du, Bo School of Computer Science Wuhan University Wuhan430072 China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan430072 China The Obstetrical Department Wuhan University People’s Hospital East Branch Wuhan430072 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430072 China

Early prenatal screening of fetal nasal bone (FNB) development is crucial for detecting chromosomal abnormalities. Existing deep learning approaches primarily focus on detection rather than diagnosis of FNB. This paper introduces an early prenatal FNB development automated diagnostic system (FNB-ADS), which employs a cascaded hierarchical filtering method to reduce noise interference in mid-sagittal plane ultrasound images. Specifically, the system employs YOLOv8 for precise FNB localization, segments the nasal bone, tip, and prenasal skin using a specially designed lightweight segmentation network, and diagnoses developmental abnormalities using Resnet34 classification methods. Furthermore, this paper has collected and publicly released the FNB-UDV dataset, which includes a detection subset and a video subset. The detection subset contains 1,007 two-dimensional ultrasound images, while the video subset comprises 12 ultrasound videos. Upon a comprehensive evaluation, the diagnostic accuracy of FNB-ADS reaches 92.37% with a processing time of 0.14 seconds per image, and the video diagnostic accuracy is 98.69% with a per-frame inference speed of 0.37 seconds in the FNB-UDV dataset. Representing the first deep-learning approach tailored specifically for early pregnancy FNB ultrasound video diagnosis, FNB-ADS significantly enhances the standardization of diagnostic procedures and reduces the dependence on subjective clinical assessments. © 2024, The Authors. All rights reserved.

关键词： Ultrasonic imaging

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Graph Sampling based Fairness-aware Recommendation over Sensitive Attribute Removal

Graph Sampling based Fairness-aware Recommendation over Sens...

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IEEE International Conference on Data Mining (ICDM)

作者： Shenghao Liu Guoyang Wu Xianjun Deng Hongwei Lu Bang Wang Laurence Yang James J. Park Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.

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Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing networks

arXiv

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arXiv 2024年

作者： Cang, Yihan Chen, Ming Pan, Yijin Yang, Zhaohui Hu, Ye Sun, Haijian Chen, Mingzhe The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211100 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China International Joint Innovation Center Zhejiang University Haining314400 China Hangzhou310027 China The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The School of Electrical and Computer Engineering The University of Georgia AthensGA30602 United States The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy transfer

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Two-Stream Regression network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Chen, Wenting Shen, Linlin Li, Xin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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TCEIP: Text Condition Embedded Regression network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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AuSSE: A Novel Framework for Security and Safety Evaluation for Autonomous Vehicles

AuSSE: A Novel Framework for Security and Safety Evaluation ...

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IEEE/IFIP International Conference on Dependable systems and networks â€“ Supplemental Volume (DSN-S)

作者： Nhung H. Nguyen Jin-Hee Cho Terrence J. Moore Seunghyun Yoon Hyuk Lim Frederica Nelson Guangdong Bai Dan Dongseong Kim School of EECS The University of Queensland Brisbane Australia Department of Computer Science Virginia Tech Falls Church VA USA Network Science Division US Army Research Laboratory Falls Church VA USA Department of Energy Engineering Korea Institute of Energy Technology Falls Church VA USA AI Graduate School Korea Institute of Energy Technology Naju-si South Korea

ISBN: (数字)9798350395709

ISBN: (纸本)9798350395716

Autonomous vehicles (AV) are becoming increasingly efficient and equipped with advanced technologies and connectivity. These include over-the-air software updates, connecting telematics data, and software-defined vehicles. However, these advancements also introduce new potential attack surfaces and increase AV security and safety risks. A critical challenge arises: How can we assess cyber attack impact on the operational safety of AV? To address this challenge, this paper proposes a novel framework named AuSSE (Autonomous Vehicle Security and Safety Evaluation).We developed a novel graphical security model called VHARM to identify attack paths and assess security risks. Additionally, we created CARLASec, an extension to the existing CARLA simulator. CARLASec enables the handling of in-vehicle networks, protocols, cyber attack injection mechanisms, and safety scores. As a result, it is suitable for a wider range of research on AV attack and defence. Additionally, recognising that AV security and safety cannot be separated, we introduce methods for comprehensive security and safety evaluations.

关键词： Solid modeling Protocols Wireless networks Telematics Software Safety Security

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Novel Generation Injection Solution for Security Constrained Optimal Power Flow of Utility Practical Transmission Utility network

Novel Generation Injection Solution for Security Constrained...

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Global Conference for Advancement in Technology (GCAT)

作者： Bhoori Singh Avdhesh Sharma Akhil Ranjan Garg Om Prakash Mahela Baseem Khan Department of Electrical Engineering MBM University Jodhpur India Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Jaipur India Innovader Research Labs Innovader IITian Padhaiwala LearnlAb Pvt. Ltd. Jaipur India Department of Electrical and Computer Engineering Hawassa University Ethiopia Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji China Department of Technical Sciences Western Caspian University Baku Azerbaijan

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The security constrained optimal power flow (SCOPF) optimally configures and dispatches the power system in normal conditions to ensure that steady-state operating conditions do not exceed the emergency ratings when contingency is observed. Suitable methods of SCOPF helps to operate the grid optimally. Hence, this paper discussed a SCOPF considering the transmission loss minimization and minimization of bus voltage deviation (BVD). Study is performed for base case of a practical transmission utility network (TUN) and proposed case TUN by addition of a thermal power plant (TPP). TPP addition improves the voltage profile of the TUN buses, minimizes BVD and transmission losses of TUN. Study is performed using the MiPower software. Proposed study is effective compared to many studies reported in literature for a practical transmission grid.

关键词： Power transmission lines Voltage Propagation losses Minimization Software Steady-state Power systems Security Power generation Load flow

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Domain Adaptation using Silver Standard Labels for Ki-67 Scoring in Digital Pathology: A Step Closer to Widescale Deployment

arXiv

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arXiv 2023年

作者： Dy, Amanda Nguyen, Ngoc-Nhu Jennifer Mirjahanmardir, Seyed Hossein Dawe, Melanie Fyles, Anthony Shi, Wei Liu, Fei-Fei Androutsos, Dimitrios Done, Susan Khademi, April Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada Keenan Research Center St. Michael’s Hospital TorontoON Canada TorontoON Canada

Deep learning systems have been proposed to improve the objectivity and efficiency of Ki-67 PI scoring. The challenge is that while very accurate, deep learning techniques suffer from reduced performance when applied to out-of-domain data. This is a critical challenge for clinical translation, as models are typically trained using data available to the vendor, which is not from the target domain. To address this challenge, this study proposes a domain adaptation pipeline that employs an unsupervised framework to generate silver standard (pseudo) labels in the target domain, which is used to augment the gold standard (GS) source domain data. Five training regimes were tested on two validated Ki-67 scoring architectures (UV-Net and piNET), (1) SS Only: trained on target silver standard (SS) labels, (2) GS Only: trained on source GS labels, (3) Mixed: trained on target SS and source GS labels, (4) GS+SS: trained on source GS labels and fine-tuned on target SS labels, and our proposed method (5) SS+GS: trained on source SS labels and fine-tuned on source GS labels. The SS+GS method yielded significantly (p © 2023, CC BY-NC-ND.

关键词： Deep learning

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Multi-Agent Reinforcement Learning in Wireless Distributed networks for 6G

arXiv

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arXiv 2025年

作者： Zhang, Jiayi Liu, Ziheng Zhu, Yiyang Shi, Enyu Xu, Bokai Yuen, Chau Niyato, Dusit Debbah, Mérouane Jin, Shi Ai, Bo Shen, Xuemin School of Electronic and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing jiaotong University Beijing100044 China School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University 639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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DrugLLM: Open Large Language Model for Few-shot Molecule Generation

arXiv

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arXiv 2024年

作者： Liu, Xianggen Guo, Yan Li, Haoran Liu, Jin Huang, Shudong Ke, Bowen Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Department of Anesthesiology Laboratory of Anesthesia and Critical Care Medicine National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu610041 China Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing100097 China

Large Language Models (LLMs) have made great strides in areas such as language processing and computer vision. Despite the emergence of diverse techniques to improve few-shot learning capacity, current LLMs fall short in handling the languages in biology and chemistry. For example, they are struggling to capture the relationship between molecule structure and pharmacochemical properties. Consequently, the few-shot learning capacity of small-molecule drug modification remains impeded. In this work, we introduced DrugLLM, a LLM tailored for drug design. During the training process, we employed Group-based Molecular Representation (GMR) to represent molecules, arranging them in sequences that reflect modifications aimed at enhancing specific molecular properties. DrugLLM learns how to modify molecules in drug discovery by predicting the next molecule based on past modifications. Extensive computational experiments demonstrate that DrugLLM can generate new molecules with expected properties based on limited examples, presenting a powerful few-shot molecule generation capacity. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecules

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