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arXiv 2022年

作者： Rayhan, Md. Abdullah-Al-Wadud, M. Ahmed, M. Helal Uddin Department Of Computer Science And Engineering Brac University Dhaka Bangladesh Department Of Software Engineering College Of Computer And Information Sciences King Saud University Riyadh Saudi Arabia Department Of Management Information Systems University Of Dhaka Dhaka1000 Bangladesh

This study aims to statistically assess the effectiveness of vaccination against SARS-CoV-2. It is indispensable to investigate the relationship between Covid-19 deadliness and vaccination in order to study the impact of vaccine in real-world. We studied rates of infection and death due to Covid-19 in different countries with respect to their levels of vaccination. People who received the required dose of vaccination were considered as fully vaccinated in this study. Based on the percentage of fully vaccinated population, countries were categorized into several groups. Though a high-level study on the vaccine effectiveness may not provide much insight for individual level differences, a global analysis is imperative to infer the influence of vaccination as a controlling measure of the pandemic. © 2022, CC BY-NC-ND.

关键词： Vaccines

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Symbolic Verification of Fuzzy Logic Models

Symbolic Verification of Fuzzy Logic Models

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IEEE International Conference on Automated software engineering (ASE)

作者： Siang Zhao Zhongyang Li Zhenbang Chen Ji Wang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China

Fuzzy logic is widely applied in various applications. However, verifying the correctness of fuzzy logic models can be difficult. This extended abstract presents our ongoing work on verifying fuzzy logic models. We treat a fuzzy logic model as a program and propose a verification method based on symbolic execution for fuzzy logic models. We have developed and implemented the environment models for the common functions and the inference rules in fuzzy logic models. Our preliminary evaluation shows the potential of our verification method.

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and Information systems, ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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Advancements in Computing (ICAC), International Conference on

作者： K.N. Dilshan H.A.C.K. Rathnayake L.P.T.L. Pathirana D.H.S.V. Dhanapala Harinda Fernando Pradeep K.W Abeygunawardhana Faculty of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

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Zero-Shot Stance Detection via Sentiment-Stance Contrastive Learning

Zero-Shot Stance Detection via Sentiment-Stance Contrastive ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiaying Zou Xuechen Zhao Feng Xie Bin Zhou Zhong Zhang Lei Tian School of Computer National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Zero-shot stance detection (ZSSD) is an important research problem that requires algorithms to have good stance detection capability even for unseen targets. In general, stance features can be grouped into two types: target-invariant and target-specific. Target-invariant features express the same stance regardless of the targets they are associated with, and such features are general and transferable. On the contrary, target-specific features will only be directly associated with specific targets. Therefore, it is crucial to effectively mine target-invariant features in texts in ZSSD. In this paper, we develop a method based on contrastive learning to mine certain transferable target-invariant expression features in texts from two dimensions of sentiment and stance and then generalize them to unseen targets. Specifically, we first grouped all texts into several types in terms of two orthogonal dimensions: sentiment polarity and stance polarity. Then we devise a supervised contrastive learning-based strategy to capture each type's common and transferable expressive features. Finally, we fuse the above-mentioned expressive features with the semantic features of the original texts about specific targets to deal with the stance detection for unseen targets. Extensive experiments on three benchmark datasets show that our proposed model achieves the state-of-the-art performance on most datasets. Code and other resources are available on GitHub 1 1 https://***/zoujiaying1995/sscl-project.

关键词： Training Analytical models Codes Fuses Semantics Benchmark testing Feature extraction

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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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MLFuse: Multi-Scenario Feature Joint Learning for Multi-Modality Image Fusion

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IEEE Transactions on Multimedia 2025年

作者： Lei, Jia Li, Jiawei Liu, Jinyuan Wang, Bin Zhou, Shihua Zhang, Qiang Wei, Xiaopeng Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Multi-modality image fusion (MMIF) entails synthesizing images with detailed textures and prominent objects. Existing methods tend to use general feature extraction to handle different fusion tasks. However, these methods have difficulty breaking fusion barriers across various modalities owing to the lack of targeted learning routes. In this work, we propose a multi-scenario feature joint learning architecture, MLFuse, that employs the commonalities of multi-modality images to deconstruct the fusion progress. Specifically, we construct a cross-modal knowledge reinforcing network that adopts a multipath calibration strategy to promote information communication between different images. In addition, two professional networks are developed to maintain the salient and textural information of fusion results. The spatial-spectral domain optimizing network can learn the vital relationship of the source image context with the help of spatial attention and spectral attention. The edge-guided learning network utilizes the convolution operations of various receptive fields to capture image texture information. The desired fusion results are obtained by aggregating the outputs from the three networks. Extensive experiments demonstrate the superiority of MLFuse for infrared-visible image fusion and medical image fusion. The excellent results of downstream tasks (i.e., object detection and semantic segmentation) further verify the high-quality fusion performance of our method. The code is publicly available at https://***/jialei-sc/MLFuse. © 1999-2012 IEEE.

关键词： Semantic Segmentation

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ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA

arXiv

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arXiv 2022年

作者： Ramakrishna, Shreyas Luo, Baiting Kuhn, Christopher B. Karsai, Gabor Dubey, Abhishek Institute for Software Integrated Systems Vanderbilt University NashvilleTN37212 United States Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany

Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the real world to avoid such events. Testing in open-world scenarios can be difficult, time-consuming, and expensive. These challenges can be addressed by using driving simulators such as CARLA instead. A key part of such tests is adversarial testing, in which the goal is to find scenarios that lead to failures of the given system. While several independent efforts in testing have been made, a well-established testing framework that enables adversarial testing has yet to be made available for CARLA. We therefore propose ANTI-CARLA, an automated testing framework in CARLA for simulating adversarial weather conditions (e.g., heavy rain) and sensor faults (e.g., camera occlusion) that fail the system. The operating conditions in which a given system should be tested are specified in a scenario description language. The framework offers an efficient search mechanism that searches for adversarial operating conditions that will fail the tested system. In this way, ANTI-CARLA extends the CARLA simulator with the capability of performing adversarial testing on any given driving pipeline. We use ANTI-CARLA to test the driving pipeline trained with Learning By Cheating (LBC) approach. The simulation results demonstrate that ANTI-CARLA can effectively and automatically find a range of failure cases despite LBC reaching an accuracy of 100% in the CARLA benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Develop a Lightweight MEC Platform Simulator

Develop a Lightweight MEC Platform Simulator

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International Russian Automation Conference (RusAutoCon)

作者： Ivan Filianin Vladimir Bogatyrev Matvey Lesonen Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

This paper reviews the use of Minikube in multi-node mode to work with the ETSI MEC Sandbox as a lightweight multi-access edge computing platform simulator (LWMECPS). The work included a comparative analysis of MEC platforms in terms of functionality, cost of deployment, and complexity of adoption. An experiment was also conducted to test LWMECPS as a MEC platform using a Python3 test application. The experiment confirmed the feasibility of using LWMECPS as a MEC platform by deploying a Kubernetes deployment based on the number of users on the simulated 4g-macro network service zones from the ETSI MEC Sandbox. Particular attention was paid to the analysis of existing scientific literature on the use of multi-access edge computing platforms in various use cases.

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SER Analysis -An Approach to enhance Human Health

SER Analysis -An Approach to enhance Human Health

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Anusha N C.M. Mohana Akshat Sharma Aryan Sharma Garv Mittal Navadhesh. M Department of IoT School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore TamilNadu India Department of Software Systems SCOPE Vellore Institute of Technology Vellore Tamil Nadu India Department of Computational Intelligence SCOPE Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

This work focuses on Speech Emotion Recognition (SER) for extracting emotional states from speech signals, encompassing universal emotions like Happiness, Anger, Neutral, Calm, Sadness, Disgust, Fearful, and Surprised. The methodology involves extracting features such as Chromogram, Mel- frequency cepstral coefficients (MFCC), Mel scaled spectrogram, Tonal Centroid and Spectral contrast, followed by classification using Artificial Neural Networks (ANN). A health-oriented aspect, is integrated in this model by providing personalized guided meditation sessions based on the user's emotional state to promote mental wellness. The main aim of this work is to broaden its reach and impact through ongoing refinement of the model and exploration of new implementation strategies, ultimately contributing to a healthier and happier society. The results of this work demonstrate promising accuracy, with the trained deep neural network (DNN) model achieving approximately 81.25% accuracy in recognizing speech emotions in comparison with the existing algorithms. DNN shows a 11%, 8%, 14% increase in accuracy with RNN & LSTM, SVM, GMM respectively.

关键词： Support vector machines Emotion recognition Accuracy Speech recognition Speech enhancement Feature extraction Cognitive informatics Mel frequency cepstral coefficient Spectrogram Long short term memory

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