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Editorial: Advanced deep learning approaches for medical neuroimaging data with limitation

Frontiers in Computational Neuroscience 2023年 17卷 1272448页

作者： Tang, Zhiri Li, Ming Hu, Ruihan Dev, Kapal Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong School of Software Harbin Institute of Technology Harbin China Department of Computer Science Munster Technological University Cork Ireland

This research topic collects the latest works focusing on approaches and analysis for medical neuroimaging data with limitations. With the increasing development of deep learning and artificial intelligence, it also attracts attention from researchers in medical and clinical fields [1]. Adopting deep learning technology in processing medical images and signals and benefiting from the powerful feature extraction ability of convolutional neural networks, deep learning-based artificial medicine has achieved great success in the past few years [2]. However, owing to the difference between natural and medical images, in other words, some specific characteristics of medical images, adopting deep learning to process medical images directly may face numerous problems. Particularly, medical imaging, including neuroimaging data, may acquire data with few annotations, low signal-to-noise ratio, or small experimental samples, resulting in serious performance degradation during data processing [3]. Hence, how to solve these issues and improve the data analysis performance is still a challenge. In recent years, some works have emerged towards these issues in medical neuroimaging data, such as semi-supervised learning [4], weakly supervised learning [5], unsupervised learning [6], transfer learning [7], and so on. In this research topic, there are also some feasible ways for medical neuroimaging data with limitations. In the first article of this topic [8], a Soft-DTW-based single-subject short-distance event-related potential averaging method is introduced for the low signal-to-noise ratio and feature extraction issues of N400 data. Specifically, N400 data is a time-domain EEG feature, which is thought to reflect the information processing ways of human brains. Leveraging the benefits of the differentiable and efficient Soft-DTW loss function, this work also involves performing partial Soft-DTW averaging within a single-subject range, effectively exploiting the advantages of the

关键词： neuroimaging analysis of insufficient data brain diseases data processing data augmentation neuroimaging application

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on software engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Scalable analysis of stop-and-go waves

arXiv

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arXiv 2024年

作者： Ji, Junyi Gloudemans, Derek Wang, Yanbing Zachár, Gergely Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States The Transportation and Power Systems Division Argonne National Laboratory United States School of Sustainable Engineering and the Built Environment Arizona State University United States Department of Mathematical Sciences Rutgers University Camden United States

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. The past 5 years have seen an explosion in the availability of large-scale traffic data containing traffic waves and complex congestion patterns, making existing approaches unsuitable for repeatable and scalable analysis of traffic waves in this data. This paper makes a first step towards addressing this shortcoming by introducing an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before;(b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane;(c) wave fronts and tails propagate at different speeds;(d) wave boundaries capture rich and non-trivial wave topologies (with

关键词： Traffic congestion

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Development of Microcontroller-Based Pure Sine Wave Inverter with Overload and Overheating Protection

Development of Microcontroller-Based Pure Sine Wave Inverter...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Andriy Holovatyy Mykhaylo Lobur Sofia Holovata Andrzej Łukaszewicz Marek Nowakowski Kostyantyn Kolesnyk Department of Computer-Aided Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Software Engineering Ukrainian National Forestry University Lviv Ukraine Department of Machinery Design and Operation Bialystok University of Technology Bialystok Poland Development Department Military Institute of Armoured and Automotive Technology Sulejowek Poland Department of Computera-Aided Design Systems Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

In the paper, hardware and software of the Arduino based pure sine wave inverter with overload and overheating protection have been developed. The inverter constantly monitors the consumption current, the battery voltage, the temperature of the power switches (radiator) and, if their parameters are normal, forms a sinusoidal voltage of 230 V, 50 Hz at the output of the inverter, similar to the network voltage. The inverter provides the display of information on the LCD screen about current and power consumption, battery voltage, temperature of the inverter power switches, as well as display of emergency icons when the permissible operating parameters are exceeded. The inverter generates sound and visual warning messages with LED indicators and buzzer. The inverter can be used as a lightweight low-power 100 W network inverter with sine-wave voltage for autonomous power supply of low-power electronics.

关键词： Temperature sensors Temperature measurement Visualization Power demand Voltage Inverters software Batteries Protection Monitoring

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Physical Layer Security of Wireless Communications: When Moving Target Defense Meets Unmanned Aerial Vehicles

SSRN

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SSRN 2024年

作者： Ghourab, Esraa M. Jaafar, Wael Naser, Shimaa Muhaidat, Sami Al-Qutayri, Mahmoud Damiani, Ernesto Center for Cyber-Physical Systems Department of Computer and Communication Engineering Khalifa University United Arab Emirates Department of Software and Information Technology Engineering École de Technologie Supérieure MontrealQCH3C 1K3 Canada 6G Research Center Khalifa University United Arab Emirates Center Department of Computer and Communication Engineering Khalifa University United Arab Emirates

In the realm of unmanned aerial vehicle (UAV) communication, the utilization of UAVs as aerial relays for ground nodes (GNs) introduces strategic flexibility, especially in scenarios where ground base stations may experience unforeseen impairments. However, this form of communication is vulnerable to eavesdropping by malicious entities due to the broadcast nature of wireless channels. In this paper, we tackle this problem by introducing a spatiotemporal diversification-based artificial noise (AN) injection strategy, known as moving target defense (MTD), aiming to confuse potential attackers without compromising legitimate communications. The proposed approach targets maximizing the average secrecy rate (ASR) by jointly optimizing the UAV's trajectory and transmit power, aligned with optimizing the MTD transmit power splitting factor between legitimate and AN signals at the GN source. The formulated problem is a non-convex mixed integer nonlinear programming (MINLP) problem due to the non-convexity of the secrecy rate. To solve it, we formulate our system as a Markov decision process, and then we propose a novel deep reinforcement learning (DRL)-based approach to enhance the ASR under various system *** results demonstrate the superiority of the proposed algorithm over benchmarks in terms of ASR and intercept probability, showcasing its effectiveness in enhancing the security of UAV-assisted communications. © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing 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 Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Reduced-Order Models of Static Power Grids Based on Spectral Clustering

Reduced-Order Models of Static Power Grids Based on Spectral...

引用

Annual North-American Power Symposium

作者： Mario D. Baquedano-Aguilar Sean Meyn Arturo Bretas Department of Electrical & Computer Engineering University of Florida Gainesville FL USA Distributed Systems Group Pacific Northwest National Laboratory Richland WA USA G2Elab Grenoble INP CNRS Université Grenoble Alpes Grenoble France

For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning. The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piece-wise quadratic approach, then the error distribution is in the order of 10- 3 .

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer Science 2023年第4期4卷 1-10页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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