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International Conference on Computer Communications and Networks (ICCCN)

作者： Sultan Alasmari Weichao Wang Aidong Lu Yu Wang CS and IT College Majmaah University Computing & Informatics Univ. North Carolina Charlotte Charlotte NC Dept. of CS Temple University Philadelphia PA

With the emergence and fast development of cloud computing and outsourced services, more and more companies start to use managed security service providers (MSSP) as their security service team. This approach can save the budget on maintaining its own security teams and depend on professional security persons to protect the company infrastructures and intellectual property. However, this approach also gives the MSSP opportunities to honor only a part of the security service level agreement. To prevent this from happening, researchers propose to use outsourced network testing to verify the execution of the security policies. During this procedure, the end customer has to design network testing traffic and provide it to the testers. Since the testing traffic is designed based on the security rules and selectors, external testers could derive the customer network security setup, and conduct subsequent attacks based on the learned knowledge. To protect the network security configuration secrecy in outsourced testing, in this paper we propose different methods to hide the accurate information. For Regex-based security selectors, we propose to introduce fake testing traffic to confuse the testers. For exact match and range based selectors, we propose to use NAT VM to hide the accurate information. We conduct simulation to show the protection effectiveness under different scenarios. We also discuss the advantages of our approaches and the potential challenges.

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Real-Time Mobile Crowd Sensing Model for Remote Detection of Flying Uavs

SSRN

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SSRN 2024年

作者： Ahmed, Ashfaq Alkadi, Ruba Belwafi, Kais Shoufan, Abdulhadi Dept. of Computer & Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates Dept. of Computer Engineering College of Computing & Informatics University of Sharjah Sharjah United Arab Emirates Khalifa University Abu Dhabi127788 United Arab Emirates

Conformance monitoring is crucial for ensuring the safe operation of unmanned aerial vehicles (UAVs). It aims to alert relevant parties to any deviations from authorized flight plans. The European Union Aviation Safety Agency mandates conformance monitoring and suggests integrating it into UAV traffic management (UTM) systems. Although traditional monitoring systems, such as wireless sensor networks, can serve this purpose, the associated expenses for installation and maintenance make them impractical for large-scale implementations. To tackle this challenge, we propose a monitoring system that capitalizes on UAV remote identification technology and mobile crowd sensing. Remote identification (RID) is becoming a global regulatory requirement. It enables ground observers to identify drones using standard mobile devices. Our solution collects RID data by gathering reports from these observers to assess UAV operations in real-time. A significant component of this approach is determining the optimal number of reports that should be considered to allow reliable and quick evaluation. While processing more reports can enhance the evaluation accuracy, it increases the computational demands and may compromise the system’s real-time performance. Moreover, in a reward-based system, processing more reports incurs higher costs. Therefore, our system uses a mechanism that adjusts the number of received and processed reports based on airspace conditions and the crowd density in the area of interest. We validated the proposed model through Monte Carlo simulations. Results indicate that, compared to a naive model, our approach outperforms in terms of received RIDs, paid rewards, and real-time performance. © 2024, The Authors. All rights reserved.

关键词： Wireless sensor networks

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SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile Devices

arXiv

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arXiv 2023年

作者： Lim, Geunsik Kang, Donghyun Ham, MyungJoo Eom, Young Ik Dept. of Electrical and Computer Engineering College of Computing and Informatics Samsung Research Dept. of Computer Engineering Sungkyunkwan University Korea Republic of Gachon University Korea Republic of Samsung Electronics South Korea Korea Republic of

Existing memory reclamation policies on mobile devices may be no longer valid because they have negative effects on the response time of running applications. In this paper, we propose SWAM, a new integrated memory management technique that complements the shortcomings of both the swapping and killing mechanism in mobile devices and improves the application responsiveness. SWAM consists of (1) Adaptive Swap that performs swapping adaptively into memory or storage device while managing the swap space dynamically, (2) OOM Cleaner that reclaims shared object pages in the swap space to secure available memory and storage space, and (3) EOOM Killer that terminates processes in the worst case while prioritizing the lowest initialization cost applications as victim processes first. Experimental results demonstrate that SWAM significantly reduces the number of applications killed by OOMK (6.5x lower), and improves application launch time (36% faster) and response time (41% faster), compared to the conventional schemes. © 2023, CC BY.

关键词： Virtual storage

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Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models

arXiv

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arXiv 2025年

作者： Taha, Ahmed M. Aly, Salah A. Darwish, Mohamed F. Dept. of CE Faculty of Engineering Egypt University of Informatics Cairo Egypt Faculty of Computing and Data Science Badya University Giza Egypt Faculty of Science Fayoum University CS&Math Branch Fayoum Egypt Dept. of Pathology Faculty of Medicine Badya University Giza Egypt

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-consuming, prone to human error, and dependent on highly specialized expertise. This paper proposes an advanced AI-driven technique to detecting glioma, meningioma, and pituitary brain tumors using YoloV11 and YoloV8 deep learning models. Methods: Using a transfer learning-based fine-tuning approach, we integrate cutting-edge deep learning techniques with medical imaging to classify brain tumors into four categories: No-Tumor, Glioma, Meningioma, and Pituitary Tumors. Results: The study utilizes the publicly accessible CE-MRI Figshare dataset and involves fine-tuning pre-trained models YoloV8 and YoloV11 of 99.49% and 99.56% accuraies;and customized CNN accuracy of 96.98%. The results validate the potential of CNNs in achieving high precision in brain tumor detection and classification, highlighting their transformative role in medical imaging and diagnostics. © 2025, CC0.

关键词： Magnetic resonance imaging

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Information Theory for Complex Systems Scientists: What, Why, & How?

arXiv

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arXiv 2023年

作者： Varley, Thomas F. Dept. of Psychological & Brain Sciences Indiana University BloomingtonIN47405 United States School of Informatics Computing & Engineering Indiana University BloomingtonIN 47405 United States

In the 21st century, many of the crucial scientific and technical issues facing humanity can be understood as problems associated with understanding, modeling, and ultimately controlling complex systems: systems comprised of a large number of non-trivially interacting components whose collective behavior can be difficult to predict. Information theory, a branch of mathematics historically associated with questions about encoding and decoding messages, has emerged as something of a lingua franca for those studying complex systems, far exceeding its original narrow domain of communication systems engineering. In the context of complexity science, information theory provides a set of tools which allow researchers to describe a variety of dependencies, including interactions between the component parts of a system, interactions between a system and it’s environment, and the mereological interaction between the parts and the "whole". In this review, we aim to provide an accessible introduction to the core of modern information theory, aimed specifically at aspiring (and established) complex systems scientists. This includes standard measures, such as Shannon entropy, relative entropy, and mutual information, before building to more advanced topics, including: information dynamics, measures of statistical complexity, information decomposition, and effective network inference. In addition to detailing the formal definitions, we also make an effort to discuss how information theory can be interpreted and to develop the intuitions behind abstract concepts like "entropy". We aim to enable interested readers to understand what information is, and how it is used, at a fundamental level to better further their own research and education. © 2023, CC BY-NC-ND.

关键词： Large scale systems

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Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records 46

Deep STI: Deep Stochastic Time-series Imputation on Electron...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Cheng, Ming-Che Hsieh, Yi-Hsien Hsu, Te-Cheng Su, Tung-Hung Lin, Che Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Taipei10617 Taiwan Division of Gastroenterolgy and Hepatology Dept. of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei Taiwan Smart Medicine and Health Informatics Program Ntu Taipei Taiwan

ISBN: (纸本)9798350371499

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs. © 2024 IEEE.

关键词： Electronic health record

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On Phase Change Rate Maximization with Practical Applications

引用

IFAC-PapersOnLine 2023年第2期56卷 5805-5810页

作者： C.-Y. Kao S. Hara Y. Hori T. Iwasaki S.Z. Khong Dept. of Electrical Engineering National Sun Yat-Sen University Taiwan Global Scientific Information and Computing Center Tokyo Institute of Technology Japan Applied Physics and Physico-Informatics Keio University Japan Dept. of Mechanical and Aerospace Engineering University of California at Los Angeles USA Independent Researcher

We recapitulate the notion of phase change rate maximization and demonstrate the usefulness of its solution on analyzing the robust instability of a cyclic network of multi-agent systems subject to a homogenous multiplicative perturbation. Subsequently, we apply the phase change rate maximization result to two practical applications. The first is a magnetic levitation system, while the second is a repressilator with time-delay in synthetic biology.

关键词： phase change rate maximization instability analysis strong stabilization

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Collaborative Inference for MEC Services Based on Multimodal Deep Neural Network

Collaborative Inference for MEC Services Based on Multimodal...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Yu Wang, Chuxuan Xiao, Yilin Lv, Zefang Xiao, Liang Ji, Xiangyang Xiamen University School of Informatics Xiamen China Xiamen University Dept. of Institute of Artificial Intelligence Xiamen China Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9798350345384

Collaborative inference can effectively ease the computational burden imposed on mobile devices by deep neural networks (DNNs), such as employing multimodal DNNs to achieve a comprehensive understanding of the environment. Although reinforcement learning (RL)-based collaborative inference adapts to dynamic environments and enhances inference performance for DNNs, especially those with chain-structured architectures, the policy selection for multimodal DNNs must account for multiple computation-intensive feature encoders. This paper proposes a hierarchical reinforcement learning (RL)-based collaborative inference scheme for multimodal DNNs that concurrently determines the partition point and identifies the appropriate DNN-based feature encoder for each modality within the mobile edge computing system. The hierarchical structure designed for policy selection has alleviated the exponential growth of the modalities. Moreover, the mobile device maintains a confidence score based on probabilities of predefined classes to assess the risk of inference policies that results in low inference accuracy and thus avoids exploring the risk policy. Simulation results demonstrate that our proposed scheme surpasses the Neurosurgeon benchmark in reducing inference latency and energy consumption, all while maintaining a high level of confidence score without significant degradation. © 2023 IEEE.

关键词： Deep neural networks

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Mental Health Professionals' Insights on Digital Health Solutions for Anxiety in the UAE

Mental Health Professionals' Insights on Digital Health Solu...

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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Mariam Al Meqbaali Sofia Ouhbi Reem K Jan Leena Amiri Fadwa Al Mugaddam Mohamed Adel Serhani Nazar Zaki Dept. Computer Science & Software Engineering CIT UAEU Al Ain UAE Dept. Information Technology Uppsala University Uppsala Sweden Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Dept. Psychiatry and Behavioral Science CMHS UAEU Al Ain UAE College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

In recent years, anxiety disorders have become more prevalent worldwide, leading to an increased demand for mental healthcare services. Barriers such as stigma and cost, limit access to these services, prompting the exploration of alternative solutions. Digital technology shows significant promise in improving access to mental healthcare services. This study aims to explore the preferences, and concerns of Mental Health Professionals (MHPs) regarding digital health solutions for self-management of anxiety disorders in the UAE via an online survey. Participation was voluntary and anonymous, with 18 MHPs taking part. The survey included 12 questions covering demographics and key design elements. Most respondents displayed positive attitudes toward integrating digital anxiety solutions in the UAE, providing feedback and highlighting considerations. 61% endorsed the use of digital technology in anxiety disorder treatment, while 94% favored mindfulness exercises for anxiety management. 72% expressed interest in utilizing technology for therapy. Notable concerns raised included privacy and dependency. Guided by MHPs insights, we propose a conceptual framework for the design of digital anxiety solutions in the UAE. This framework prioritizes cultural appropriateness and language accessibility, offering solutions in Arabic and English. It encompasses diverse features, including a blended approach, treatment approaches, and relaxation techniques, to address anxiety disorders and ensures user safety through risk assessment and management. This framework can assist developers of digital anxiety solutions by highlighting the features and elements that MHPs deem most important.

关键词： Surveys Privacy Anxiety disorders Medical treatment Mental health Safety Electronic healthcare

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Proactive Network Traffic Prediction using Generative Adversarial Network

Proactive Network Traffic Prediction using Generative Advers...

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International Conference on Information Networking

作者： Gyurin Byun Van-Vi Vo Syed M. Raza Duc-Tai Le Huigyu Yang Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of Superintelligence Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Proactive traffic management in 5G networks is crucial for optimizing network efficiency, ensuring quality of service, adapting to dynamic conditions, and enhancing the user experience. In this paper, we propose a Generative Adversarial Network (GAN) architecture that leverages spatiotemporal features in network traffic data to predict future traffic. Our approach incorporates a Convolutional Long Short-Term Memory (ConvLSTM) model within the generator of the GAN, which collaborates with the discriminator, utilizing a Convolutional Neural Network (CNN) model, to provide essential feedback for training the generator. This integration ensures that our model not only predicts future traffic with improved accuracy but also adapts to dynamic network conditions. Based on experimental results using network traces, our model significantly outperforms the baseline, reducing prediction error by 12% while forecasting network traffic for the next 1 minute. These findings represent a significant advancement in proactive network management, particularly in addressing the challenges posed by real-time streaming and other latency-sensitive applications in 5G networks.

关键词： Training Adaptation models 5G mobile communication Telecommunication traffic Predictive models Generative adversarial networks Generators

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