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arXiv 2024年

作者： Jiang, Xuemei Guo, Yangyang Zhang, Yue Song, Yanjie Pedrycz, Witold Xing, Lining School of Electronic Engineering Xidian University Xi’an China School of Systems Science Beijing Jiaotong University Beijing China School of Reliability and Systems Engineering Beihang University Beijing China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission in this network primarily relies on two modes: inter-satellite communication and satellite-to-ground station communication. The latter directly impacts the successful reception of data by users. However, due to resource and task limitations, finding a satisfactory solution poses a significant challenge. The communication satellite-ground station network scheduling problem (CS-GSNSP) aims to optimize CSN effectiveness by devising a plan that maximizes link construction time while considering constraints associated with satellite operation modes. The large number of tasks and numerous constraints in the problem result in a time-consuming evaluation of fitness function values. To address this issue, we propose a fuzzy fitness evaluation method (FFEM) that employs fuzzy or real evaluation methods based on individual similarity degrees. Additionally, we introduce an evolutionary algorithm based on FFEM, called evolutionary algorithm based on FFEM (FFEEA), for iteratively searching high-quality network construction schemes. In FFEEA, an adaptive crossover approach is used for efficient population search. Finally, extensive experiments are conducted to demonstrate that our proposed fuzzy fitness evaluation method and other improvement strategies significantly enhance satellite network service time. The study introduces a novel approach to enhance the efficiency of solving combinatorial optimization problems, such as CS-GSNSP, by mitigating the complexity associated with fitness evaluation. © 2024, CC BY-NC-ND.

关键词： Combinatorial optimization

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Correction: STASiamRPN: visual tracking based on spatiotemporal and attention

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Multimedia systems 2022年第5期28卷 1557-1558页

作者： Wu, Ruixu Wen, Xianbin Liu, Zhanlu Yuan, Liming Xu, Haixia School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Department of Physical Education Tianjin University of Technology Tianjin China

During online tracking, we use 5 frames as a video sequence, which includes the first 4 frames and the last frame. We use the last frame as the tracking frame. If the video sequence was less than 5 frames, we repeated...

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A Data science Approach to Precision Medicine: Allostatic Load as a Predictor of Cardiovascular Disease

A Data Science Approach to Precision Medicine: Allostatic Lo...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Otto M. Ikome Venkata K. Melapu Tor A. Kwembe Ella Watson Department of computer science engineering and Technology Jackson State University Jackson USA Department of Mathematics and Statistical Sciences Jackson State University Jackson USA Jackson Public school system Jackson State University Jackson USA

Introduction: Precision medicine, tailored to individual genetic and environmental attributes, has emerged as a practical approach in healthcare. Allostatic load (AL), reflecting the body's response to stressors, holds promise in predicting susceptibility to chronic illnesses like cardiovascular disease (CVD). This study explored the relationship between CVD and AL, employing big data analytics and machine learning to enhance prediction-accuracy. Objectives: 1. Develop a predictive model using AL measures for CVD prediction. 2. Create a clinical AL tool for personalized medicine. 3. Demonstrate the relationship between AL scores and CVD. Methods: Data from the National Center for Health Statistics encompassed demographic, health, and clinical variables. AL was computed using a count-based method, considering factors like blood pressure, body mass index, glucose, lipids, etc. Machine learning (ML) algorithms, including random forest and logistic regression, were employed to predict CVD based on AL factors. Missing data were managed using imputation methods tailored to variable distributions. Results: The count-based AL method correlated more with age than the weighted method. Both methods showed significant correlations with age, emphasizing the association between AL and aging. AL was positively correlated with CVD, with an 82% accuracy rate in predicting CVD using the random forest algorithm. Black/African American populations displayed the highest correlation between AL and CVD. Conclusions: This study highlights the potential of AL as a predictor for CVD and supports its integration into clinical practice. AL provides a comprehensive view of physiological health and empowers healthcare providers to make informed decisions, particularly in vulnerable populations. Big data analytics and ML enhance prediction accuracy and offer insights into the complex factors contributing to CVD risk. The convergence of healthcare, statistics, and computer programming is essential

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New Approach for Efficient Malicious Multiparty Private Set Intersection

SSRN

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SSRN 2023年

作者： Lv, Siyi Wei, Yu Jia, Jingyu Li, Xinhao Liu, Zheli Chen, Xiaofeng Guo, Liang College of Cyber Science The College of Computer Science Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China Department of Computer Science Purdue University United States School of Cyber Engineering Xidian University Xi’an China Huawei Technology Co. Ltd Shenzhen China

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. However, existing solutions face challenges in terms of security and efficiency. Some schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted *** paper introduces a novel scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard UC framework against malicious adversaries. To demonstrate the efficiency of TH-PSI, we implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as $2^{22}$. We compare TH-PSI with state-of-the-art schemes, ENOC21 and NTY21. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants. © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Deep Unsupervised Learning-Based Classification of Damages and Their Severity in Milled Rice Using High-Magnification Images

SSRN

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SSRN 2023年

作者： Moses, Kriz Bhupendra Kankar, Hitarth Miglani, Ankur Microfluidics and Droplet Dynamics Lab Department of Mechanical Engineering Indian Institute of Technology Madhya Pradesh Indore453552 India System Dynamics Lab Department of Mechanical Engineering Indian Institute of Technology Madhya Pradesh Indore453552 India Department of Computer Science Birla Institute of Technology and Science Pilani Hyderabad Campus India

The quality evaluation of processed rice grains is an important factor in determining market acceptance, pricing, storage stability, processing quality, and overall consumer approval. In the literature, the machine vision methods are predominantly based on supervised machine learning which rely on manual labelling, and therefore, face issues such as time intensiveness, subjectiveness and overlapping classes. Therefore, in this study, a deep unsupervised method Contrastive-RC is developed that leverages contrastive self-supervised learning technique for fine-grained damage classification of processed white rice. Particularly, contrastive-RC involves a combination of SimCLR, UMAP, and HDBSCAN, where self-supervised contrastive learning (SimCLR) is used for feature representation followed by dimensionality reduction (UMAP) and clustering (HDBSCAN). To enable this, a dataset of 20,134 high magnification images (24 MP at 3.8 µm/pixel) of individual rice grains that is spread across six different types of surface damages is developed. It is demonstrated that the Contrastive-RC is successful in clustering the rice grains into six main classes with well-defined attributes. The class names along with the number of corresponding instances are normal-damage (5599), chalky-discoloured (4987), discoloured (3215), half-chalky (2386), healthy (2061), and broken (931). It is demonstrated that the contrastive-RC method can be extended to subclassify these damages based on the damage severity by providing a low-level control, thereby enabling the method to be used in multiple use-cases. The method is fast, versatile and robust towards changes in variables like brightness and grain orientation, thus making it ideal for real world use and extension to other varieties of white processed rice. © 2023, The Authors. All rights reserved.

关键词： Quality control

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent system (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Review of the issues and a thorough investigation of biometric authentication systems

Review of the issues and a thorough investigation of biometr...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Prashant Johri Ms. Sonia Arora Department of Computer Applications Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Business System Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India

A computer system's user authentication process is based on security mechanisms including passwords, keys, ID cards, and pins, among others. Nonetheless, as innovation propels day to day, there is likewise an expansion in the misappropriation and robbery of these safety efforts. By applying biometrics innovation, an individual can be verified in light of “what his identity is” as opposed to a component vector got from physiological or potentially social qualities of a person, which is the way biometric security systems became. All the arising biometric innovations and the hardships in every innovation have been completely depicted in this paper to help tenderfoots in picking their exploration subject or a particular biometric innovation. Also, future improvements in biometric innovation are thought of. The main factor in determining whether a claim made by the real entity is true is authentication. Biometrics is one approach to increase authentication's sturdiness among other methods. Since the last decade, biometric technology has been widely used and recognised worldwide to verify a person's identity. Additionally, the technology employed solves the drawbacks of the conventional authentication procedure, such as knowledge-based problems with password and token authentication of an individual. The proposed methodology implemented Convolution neural network with brain organization which will be utilized for the preparation of picture tests and a pre prepared organization of brain can be used in CNN which could eliminate handling time. In results the training accuracy is 6.3 and validation accuracy is 5.3.

关键词： Training Technological innovation Biometrics (access control) Authentication Passwords Organizations Medical services

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Abstraction-Based Verification of Approximate Pre-Opacity for Control systems

arXiv

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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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