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arXiv 2024年

作者： Kong, Chenqi Luo, Anwei Bao, Peijun Yu, Yi Li, Haoliang Zheng, Zengwei Wang, Shiqi Kot, Alex C. Lab School of Electrical and Electronic Engineering Nanyang Technology University Singapore639798 Singapore The School of Computer Science and Engineering Sun Yatsen University Guangzhou China The Department of Electrical and Engineering City University of Hong Kong Hong Kong The Department of Computer Science and Computing Zhejiang University City College Zhejiang China The Department of Computer Science City University of Hong Kong Hong Kong

Deepfakes have recently raised significant trust issues and security concerns among the public. Compared to CNN face forgery detectors, ViT-based methods take advantage of the expressivity of transformers, achieving superior detection performance. However, these approaches still exhibit the following limitations: (1) Fully fine-tuning ViT-based models from ImageNet weights demands substantial computational and storage resources;(2) ViT-based methods struggle to capture local forgery clues, leading to model bias;(3) These methods limit their scope on only one or few face forgery features, resulting in limited generalizability. To tackle these challenges, this work introduces Mixture-of-Experts modules for Face Forgery Detection (MoE-FFD), a generalized yet parameter-efficient ViT-based approach. MoE-FFD only updates lightweight Low-Rank Adaptation (LoRA) and Adapter layers while keeping the ViT backbone frozen, thereby achieving parameter-efficient training. Moreover, MoE-FFD leverages the expressivity of transformers and local priors of CNNs to simultaneously extract global and local forgery clues. Additionally, novel MoE modules are designed to scale the model’s capacity and smartly select optimal forgery experts, further enhancing forgery detection performance. Our proposed learning scheme can be seamlessly adapted to various transformer backbones in a plug-and-play manner. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art face forgery detection performance with significantly reduced parameter overhead. The code is released at: https://***/LoveSiameseCat/MoE-FFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Predicting Stock Market Trends Using Deep Learning: A Case Study of Pakistan Stock Exchange 18

Predicting Stock Market Trends Using Deep Learning: A Case S...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Balouch, Maaz Hamayat, Faizan Abbasi, Humza Fazal Javed, Muhammad Qasim Mehrban, Ali Ahmad, Rana Fayyaz Department of Business Studies Namal University Mianwali Pakistan National Center for Physics Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan School of Electrical and Electronics Engineering Newcastle University Newcastle United Kingdom

ISBN: (纸本)9798331508692

This research investigates the short-term stock price prediction in the Pakistan Stock Exchange (PSX) using Deep Learning (DL) techniques. By integrating historical stock data, economic indicators, and market news, the research aims to address the limitations of traditional stock analysis methods. Data was collected and pre-processed from various sources, including the PSX, economic indicators, and market news. Four techniques were implemented: ARIMA, LSTM, BI-LSTM, and a hybrid CNN-BI-LSTM. Their performance was evaluated using RMSE, MAE, and MAPE metrics. Comparative analysis revealed that BILSTM outperformed the other techniques, with RMSE, MAE, and MAPE scores of 0.9298, 0.6099, and 13.9443, respectively. Despite ARIMA's lower accuracy, it had the fastest inference time, followed by LSTM, CNN-BI-LSTM, and BI-LSTM. Results indicate that using comprehensive data with DL models significantly improves stock price forecast accuracy, highlighting the potential of technological innovation in enhancing financial market decision-making. These DL algorithms can provide valuable insights for investors and analysts in the PSX, leading to better decision-making and more accurate economic predictions. © 2024 IEEE.

关键词： Deep learning

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Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity

arXiv

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arXiv 2023年

作者： Zhou, Zhanpeng Yang, Yongyi Yang, Xiaojiang Yan, Junchi Hu, Wei Dept. of Computer Science and Engineering MoE Key Lab of AI Shanghai Jiao Tong University China Dept. of Electrical Engineering & Computer Science University of Michigan United States

Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path in the parameter space while maintaining near-constant training and test losses. In this work, we introduce a stronger notion of linear connectivity, Layerwise Linear Feature Connectivity (LLFC), which says that the feature maps of every layer in different trained networks are also linearly connected. We provide comprehensive empirical evidence for LLFC across a wide range of settings, demonstrating that whenever two trained networks satisfy LMC (via either spawning or permutation methods), they also satisfy LLFC in nearly all the layers. Furthermore, we delve deeper into the underlying factors contributing to LLFC, which reveal new insights into the permutation approaches. The study of LLFC transcends and advances our understanding of LMC by adopting a feature-learning perspective. We released our source code at https://***/zzp1012/LLFC. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Resonating with Cellular Pathways:Transcriptome Insights into Nonthermal Bioeffects of Middle Infrared Light Stimulation and Vibrational Strong Coupling on Cell Proliferation and Migration

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Research 2024年第4期2024卷 789-800页

作者： Xingkun Niu Zhongyu Wu Feng Gao Shaojie Hou Shihao Liu Xinmin Zhao Liping Wang Jun Guo Feng Zhang Quantum Biophotonic Lab Key Laboratory of Optical Technology and Instrument for MedicineMinistry of EducationSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Department of Nuclear Medicine The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan HospitalJinan 250013China School of Radiology Shandong First Medical University and Shandong Academy of Medical SciencesJinan 250024China The School of Biomedical Engineering Guangzhou Medical UniversityPanyu DistrictGuangzhou 511436China

Middle infrared stimulation(MIRS)and vibrational strong coupling(VSC)have been separately applied to physically regulate biological systems but scarcely compared with each other,especially at identical vibrational frequencies,though they both involve resonant *** cell proliferation and migration as typical cell-level models,herein,we comparatively studied the nonthermal bioeffects of MIRS and VSC with selecting the identical frequency(53.5 THz)of the carbonyl *** found that both MIRS and VSC can notably increase the proliferation rate and migration capacity of *** sequencing results reflected the differential expression of genes related to the corresponding cellular *** work not only sheds light on the synergistic nonthermal bioeffects from the molecular level to the cell level but also provides new evidence and insights for modifying bioreactions,further applying MIRS and VSC to the future medicine of frequencies.

关键词： stimulation Middle identical

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Cybersecurity in local governments: A systematic review and framework of key challenges

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Urban Governance 2025年第1期5卷 1-19页

作者： Hossain, Sk Tahsin Yigitcanlar, Tan Nguyen, Kien Xu, Yue City 4.0 Lab School of Architecture and Built Environment Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Electrical Engineering and Robotics Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Computer Science Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia

Cybersecurity has emerged as a critical concern in modern urban governance, as local governments now extend their responsibilities beyond traditional infrastructure management to safeguard digital systems and citizen data in increasingly digitised smart city environments. Despite the importance of robust cybersecurity measures, there is a concerning gap—i.e., local governments often exhibit a cybersecurity posture that is inadequately prepared to counter the rising cyber threats. This discrepancy underscores the urgency for a comprehensive understanding of the challenges restricting effective cybersecurity in local governmental settings. This paper aims to elucidate these challenges of local governments in a structured framework utilising a systematic literature review method named the PRISMA protocol. The findings reveal that the stakes are high when it comes to cybersecurity for local governments, with the key challenges being financial resource constraints, technological vulnerabilities, human factors, and the regulatory framework. This study recommends improving interdepartmental coordination, adopting relevant policies, and executing targeted cybersecurity training to address these challenges. The study also highlights unconventional approaches to mitigate financial and skill resource limitations, including the utilisation of open-source security tools, outsourcing specific cybersecurity functions, and establishing collaborations with local cybersecurity agencies, academic institutions, and the private sector to obtain expertise and advanced resources. Overall, this study offers valuable insights into the critical cybersecurity challenges faced by local governments and provides specific recommendations, equipping urban policymakers, administrators, and researchers with valuable guidance to enhance cybersecurity resilience within this critical yet understudied research domain. © 2024 The Author(s)

关键词： Cyber threat Cybersecurity Local government Municipality Smart city Urban governance

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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

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Digital Communications and Networks 2022年第6期8卷 900-910页

作者： M.S.Mekala Gautam Srivastava Ju H.Park Ho-Youl Jung Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38544South Korea RLRC LAb for Autonomous Vehicle Parts and Materials Innovation Yeungnam UniversityGyeongsan38544South Korea Department of Math and Computer Science Brandon UniversityBrandonCanada Department of Electrical Engineering Yeungnam UniversityGyeongsanSouth Korea Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and *** services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and *** issues lead to computation and communication overhead of servers and computation *** address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and ***,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication ***,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge *** simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

关键词： Edge computing Adaptive trust weight(ATW)model Quotient user-centric coeval-learning(QUCL)mechanism Deep learning Service reliability

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A Long-Term Voltage Stability Margin Index Based on Multiple Real Power Flow Solutions

A Long-Term Voltage Stability Margin Index Based on Multiple...

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IEEE General Meeting Power& Energy Society

作者： Bin Wang Dan Wu Xiaowen Su Kai Sun Le Xie Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX School of Electrical and Electronic Engineering Huazhong University of Science and Technology Lab for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA Department of Electrical Engineering and Computer Science University of Tennessee TN Department of Electrical and Computer Engineering Taxes A&M University College Station TX

In this paper, we demonstrate the distribution of real-valued power flow solutions and its application to the long-term voltage stability. It is the first time in power engineering we realize that even a small-scale IEEE standard power system can admit a humongous number of real-valued solutions for a single load and generation profile. For example, the IEEE 30-bus system can achieve 25686 many different real-valued solutions at a light loading condition. Furthermore, a mysterious class of “false” power flow solutions is reported and analyzed rigorously as legitimate numerical solutions. All solution sets investigated in this paper are posted online associated with this paper to support potential future applications [1]. Based on these extensive solved power flow solutions, we exhibit their occurrence patterns and distributions at different loading levels, and propose a long-term voltage stability margin index to quantify the long-term voltage stability of a given power flow condition. Numerical studies on a 5-bus system and a 57-bus system show the feasibility and effectiveness of the proposed index in assessing power system long-term voltage stability margin.

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Estimating the Composition Ratios of Network Services Carried in Mixed Traffic

SSRN

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SSRN 2023年

作者： Xie, Yi Wu, Zihui Tang, Shensheng Liu, Xingcheng School of Computer Science and Engineering Guangdong Key Laboratory of Information Security Sun Yet-sen University Guangzhou510275 China Department of Electrical and Computer Engineering St Cloud State University St Cloud MN56301 United States School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China

Network performance management (NPM) and quality of service (QoS) guarantee are the key concerns of network operators and service providers/consumers, respectively. The basis for traffic scheduling is one of the vital elements guiding the implementation of both NPM and QoS assurance. Although traffic identification technologies can theoretically be applied to providing basis for traffic scheduling, the computational complexity caused by the flow-by-flow processing makes them unsuitable for actual large-scale high-throughput network scenarios. In this work, we introduce a new scheme to provide a basis for service-oriented NPM and QoS guarantee. The proposed scheme treats the mixed traffic as a whole and estimates the composition ratios of network services carried in mixed traffic, which makes traffic scheduling for NPM and QoS more reasonable and flexible. To estimate the composition ratios, the observed mixed traffic is expressed as a bipartite graph according to the communication relationship. Then, the bipartite graph is further projected to a multi-dimensional hyper-image based on node coding, random walking and embedding mapping. By this way, we convert the service composition ratio estimation into a problem similar to image classification and enable it to be solved by general machine learning methods like convolutional neural networks. The proposed scheme facilitates batch traffic scheduling and can reorganize the distribution of compositions of network services for each link based on given management strategies. Its computational complexity is significantly lower than that of flow-by-flow processing methods. We validate the proposed solution via real network data and evaluate its performance with some benchmark methods based on traffic identification. The experimental results show that the proposed solution can accurately infer the service composition ratios of mixed traffic with low computational complexity. © 2023, The Authors. All rights reserved.

关键词： Computational complexity

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SpatialHD: Spatial Transformer Fused with Hyperdimensional Computing for AI Applications

SpatialHD: Spatial Transformer Fused with Hyperdimensional C...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Meriem Bettayeb Eman Hassan Baker Mohammad Hani Saleh Electrical Engineering and Computer Science Department System on Chip Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional Computing (HDC) and Vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. This paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. Then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. This further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model’s functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC.

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications

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Procedia computer science 2025年 252卷 231-240页

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Sr. Principal Systems Engineer in C&RS Platform services DT Amtrak USA University of south alabama Department of electrical engineering Masters in electrical engineering Senior Software Engineer in Cloud platform services City Of Philadelphia Pennsylvania USA Vice president Neuberger Berman US Software Development & Engineering in Content Discovery & AI Comcast USA Troy University Montgomery Campus AL. Department of computer Science Masters In Computer Science Department of Information Technology GEICO - Chevy Chase Chevy Chase MD 20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Resource Management Industrial Internet of Things Applications Complex Decision Making Adaptive Deep Reinforcement Learning Ship Rescue Optimization

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