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arXiv 2022年

作者： Hou, Junyao Liu, Siyuan Yin, Xiang Zamani, Majid Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University China Department of Electrical and Computer Engineering Technical University of Munich Germany Department of Computer Science LMU Munich Germany Department of Computer Science University of Colorado Boulder United States

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Preopacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions. Copyright © 2022, The Authors. All rights reserved.

关键词： Opacity

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Haozhao Wang Wenchao Xu Yunfeng Fan Ruixuan Li Pan Zhou School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong 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

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods.

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Monkeypox Lesion Classification: A Transfer Learning Approach for Early Diagnosis and Intervention

Monkeypox Lesion Classification: A Transfer Learning Approac...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Abdullah Al Noman Hasib Fardin Gunjan Chhabra Shayma Sultana Rezaul Haque Md. Redwan Ahmed Swarna Hasibunnahar Keshav Kaushik Department of Information Technology Westcliff University USA Department of Engineering Management Westcliff University USA Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Department of Management Information System International American University USA Department of Computer Science and Engineering East West University Bangladesh Department of Life Sciences Huzhou University China Amity School of Engineering and Technology Amity University Mohali Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Monkeypox is a viral disease found in Central and West Africa, and it presents notable public health challenges due to its clinical similarity to smallpox. Recent outbreaks in over 40 countries have underscored the urgent need for quick and accurate diagnosis, especially in settings with limited resources where traditional diagnostic methods may be inadequate. This study explores the application of deep learning techniques to classify four specific types of monkeypox skin lesions. Transfer learning was employed to evaluate the performance of different models using a dataset of 770 skin lesion images. Various methods, including data augmentation and image preprocessing, were utilized to improve the effectiveness of the models. The results indicate that the custom SqueezeNet model was the most successful, achieving an accuracy of ${9 8. 8 5 \%}$ and an F1 score of ${9 8. 8 9 \%}$. These results surpass previous research benchmarks across four key evaluation measures. Additionally, the model was integrated into a web-based diagnostic tool capable of reliably detecting different forms of monkeypox. This study represents a significant advancement in utilizing artificial intelligence for disease detection, offering a practical approach for early and accurate monkeypox diagnosis. The proposed system has thex potential for seamless integration into clinical practice, aiding healthcare professionals in timely interventions.

关键词： Accuracy Soft sensors Transfer learning Skin Lesions Reliability Public healthcare Viruses (medical) Medical diagnostic imaging Diseases

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A Scalable Solution for Pneumonia Diagnosis: Transfer Learning for Chest X-ray Analysis

A Scalable Solution for Pneumonia Diagnosis: Transfer Learni...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Rezaul Haque Shayma Sultana Chandan Prasad Sayed Hasan Hasib Fardin Abdullah Al Sakib Neeru Malik Keshav Kaushik Department of CSE East West University Dhaka Bangladesh Department of Management Information System International American University USA Department of Computer Science and Engineering Pimpri Chinchwad University Pune Maharashtra India Department of Engineering Management Westcliff University USA Department of Information Technology Westcliff University USA School of Engineering & Technology Pimpri Chinchwad University Pune Maharashtra India Amity School of Engineering and Technology Amity University Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The high death rate and rapid spread of pneumonia make it a serious global health concern. The precision of traditional diagnostic techniques is often compromised, particularly in resource-limited settings. Existing studies have predominantly focused on either binary or multi-class classification, often relying on limited datasets, which affects the generalizability and robustness of models. Additionally, the interpretability and transparency of deep learning models remain challenging in clinical environments. Addressing these challenges is crucial for advancing diagnostic methodologies to combat pneumonia. This study explores the use of transfer learning to improve the reliability of pneumonia classification using chest X-ray images. An automated classification system was developed, utilizing a combination of image preprocessing techniques, feature extraction with GLRLM, and various machine learning and transfer learning algorithms. Two datasets were employed: the GWCMCx dataset with 5856 chest X-rays and another dataset comprising 9207 chest X-rays. The system was designed for both binary and multi-class classification scenarios. CapsuleNet emerged as the most effective model, achieving an accuracy of ${9 9. 0 2 \%}$ in binary classification and $90.43 \%$ in multi-class classification, significantly outperforming previous studies. Additionally, a user-friendly web application was developed to facilitate the integration of the classification system into clinical workflows, accelerating clinical decision-making. This study demonstrates the potential of transfer learning to automate and improve pneumonia diagnosis, offering a scalable solution that can alleviate radiologists’ workload and improve patient outcomes.

关键词： Training Pneumonia Accuracy Transfer learning Decision making Mortality Robustness Medical diagnosis X-ray imaging Medical diagnostic imaging

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HiCRISP: An LLM-based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner

arXiv

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arXiv 2023年

作者： Ming, Chenlin Lin, Jiacheng Fong, Pangkit Wang, Han Duan, Xiaoming He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Department of Computer Science University of Illinois Urbana-Champaign IL United States The Department of Engineering Science University of Oxford Oxford United Kingdom

The integration of Large Language Models (LLMs) into robotics has revolutionized human-robot interactions and autonomous task planning. However, these systems are often unable to self-correct during the task execution, which hinders their adaptability in dynamic real-world environments. To address this issue, we present an LLM-based Hierarchical Closed-loop Robotic Intelligent Self-correction Planner (HiCRISP), an innovative framework that enables robots to correct errors within individual steps during the task execution. HiCRISP actively monitors and adapts the task execution process, addressing both high-level planning and low-level action errors. Extensive benchmark experiments, encompassing virtual and real-world scenarios, showcase HiCRISP’s exceptional performance, positioning it as a promising solution for robotic task planning with LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Fog-Cloud-IoT Enabled WSN Architecture 3

Fog-Cloud-IoT Enabled WSN Architecture

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Anitha Christy Angelin, P. Samuel Devakumar, P.V. Al-Farhani, Liwa H. Kumar, Gandam Vijay Deep, Shaurya Mohammad, Shoaib PSNA College of Engineering and Technology Tamilnadu Dindigul India Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India RUDN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Career Point University Computer Science and Engineering Rajasthan India IIMT College of Management Greater Noida India IMS Unison University Department of Law Uttarakhand Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge. © 2022 IEEE.

关键词： Fog computing

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Smart Agriculture Prediction system for Vegetables Grown in Sri Lanka 12

Smart Agriculture Prediction System for Vegetables Grown in ...

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12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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Surgical Instrument Detection for Laparoscopic Surgery using Deep Learning 19

Surgical Instrument Detection for Laparoscopic Surgery using...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Boonkong, Apiwat Hormdee, Daranee Sonsilphong, Suphachoke Khampitak, Kovit Khon Kaen University Embedded System RandD Group Computer Engineering Khon Kaen Thailand Khon Kaen University Data Science and Health Informatics Program Academic Affairs Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kaen Thailand

ISBN: (纸本)9781665485845

Laparoscopic surgery has transformed conventional open surgery. Robot-Assisted laparoscopic surgery which is minimally invasive is effective for operations in limited space. Nevertheless, the robotic system which is utilized in such surgery can inflict impair to the surgical instruments, organs, or tissues as a consequence of the narrow field of view and space available to operate, and insufficient tactile response. In laparoscopy, the surgeon observes what he operates via digital images which are displayed on a monitor. In addition to the surgeon's expertise, the image-guided surgery system could offer him significant support if that system is capable of understanding those images. computer Vision has been able to proceed due to the emergence of Machine Learning, and especially Deep Learning. This paper applies Deep Learning for surgical instrument detection for laparoscopic surgery. The fastest model, based on results trained on the COCO 2017 Dataset on TensorFlow Framework, for each of 4 algorithms-Faster-RCNN, SSD, CenterNet and EfficientDet-from Model Zoo, along with YOLOv4 on Darknet Framework, have been investigated in this work. The evaluation metrics used are Precision, Recall and F1-score. The results reveal that each model plays its part well;however, YOLOv4 seemed to breakthrough this statement by offering grate result for all three accuracy metrics. © 2022 IEEE.

关键词： Laparoscopy

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Trend and Correlation Analysis of Instagram Activity Using Data Mining and Statistics

Trend and Correlation Analysis of Instagram Activity Using D...

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International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Wilem Musu Indra Samsie Aldi Bastiatul Fawait Nadia Lempan Nurliah Maechel Maximiliano Gabriel Sinar Department of Informatics Engineering Dipa Makassar University Makassar Indonesia Department of Information System Dipa Makassar University Makassar Indonesia Department of Computer Science Widya Gama Mahakan University Samarinda Indonesia Department of Digital Business Dipa Makassar University Makassar Indonesia Department of Entrepreneurship Dipa Makassar University Makassar Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

With the growing importance of social media for brand engagement, there remains a limited understanding of how specific metrics-such as Reach, Followers, and New Followers-impact Instagram user interaction over time. This research seeks to address this gap by examining which variables most effectively drive Engagement on Instagram, providing insights for strategic content optimization. Through an analysis of 100 posts, using multiple linear regression and decision tree methods, the study investigates the relationship between Reach, Followers, New Followers, and Engagement. Results indicate that Reach, Followers, and New Followers are not statistically significant predictors of Engagement, as evidenced by high p-values (all> 0.05). Decision tree results demonstrate a Mean Absolute Error (MAE) of 0.0226 and an $\mathrm{R}^{2}$ Score of 0.8003, suggesting moderate predictive accuracy but with potential for refinement. Moving average analysis reveals fluctuating Engagement with no stable long-term trend. A correlation matrix further shows that Reach and New Followers exhibit high correlations with Engagement (0.9723 and 0.9707, respectively), while Followers show a weak correlation (0.2575). These findings highlight the limited impact of Followers on Engagement, underscoring the importance of Reach and New Followers. This study contributes novel insights by identifying key engagement drivers, emphasizing the need for more targeted strategies in Instagram campaign planning to enhance user interaction.

关键词： Correlation Social networking (online) Linear regression Market research Timing Planning Web sites Multimedia communication Decision trees Optimization

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An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM systems

An Efficient Deep Network for Modulation Classification in I...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Thien Huynh-The Quoc-Viet Pham Toan-Van Nguyen Daniel Benevides Da Costa Van-Phuc Hoang Department of Computer and Communication Engineering HCMC University of Technology and Education Vietnam Institute of System Integration Le Quy Don Technical University Vietnam School of Computer Science and Statistics Trinity College Dublin Ireland School of Computer Science and Engineering International University - VNU HCM City Vienam Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates

In this paper, an efficient automatic modulation classification for MIMO-OFDM signals is proposed for next generation wireless networks by exploiting cutting-edge deep learning techniques. Particularly, we design a deep network, namely OFDM modulation classification network (OMCNet), with asymmetric depthwise separable convolution, residual connection, and attention mechanism in a sophisticated design of processing blocks to reduce the overall complexity without sacrificing learning efficiency. Relying on the simulations, our deep network achieves over 92% for different delay spread models demonstrates the robustness of modulation classification under various channel impairments. Remarkably, compared with a baseline model, OMCNet reduces the network size by four times and computation cost by two times with the asymmetric depthwise separable while achieving a competitive accuracy thanks to residual connection and attention mechanism.

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