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arXiv 2024年

作者： Li, Xingyu Tang, Bo Department of Computer Science Tulane University New Orleans United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester United States

Deep neural networks suffer from the catastrophic forgetting problem in the field of continual learning (CL). To address this challenge, we propose MGSER-SAM, a novel memory replay-based algorithm specifically engineered to enhance the generalization capabilities of CL models. We first intergrate the SAM optimizer, a component designed for optimizing flatness, which seamlessly fits into well-known Experience Replay frameworks such as ER and DER++. Then, MGSER-SAM distinctively addresses the complex challenge of reconciling conflicts in weight perturbation directions between ongoing tasks and previously stored memories, which is underexplored in the SAM optimizer. This is effectively accomplished by the strategic integration of soft logits and the alignment of memory gradient directions, where the regularization terms facilitate the concurrent minimization of various training loss terms integral to the CL process. Through rigorous experimental analysis conducted across multiple benchmarks, MGSER-SAM has demonstrated a consistent ability to outperform existing baselines in all three CL scenarios. Comparing to the representative memory replay-based baselines ER and DER++, MGSER-SAM not only improves the testing accuracy by 24.4% and 17.6% respectively, but also achieves the lowest forgetting on each benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： Image classification

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GUI: A Comprehensive Dataset of Global Urban Infrastructure Based on Geospatial Visual Foundation Models 24

GUI: A Comprehensive Dataset of Global Urban Infrastructure ...

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Han, Zhenyu Zhang, Xin Xi, Yanxin Luo, Yan Xia, Tong Li, Yong Department of Electronic Engineering BNRist Tsinghua University Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science University of Helsinki Helsinki Finland Media Lab Massachusetts Institute of Technology Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400711077

The substantial social and financial costs of infrastructure identification impede in-depth analyses of sustainable urban design, especially in developing countries. In this paper, we present a novel framework with interactive web visualization based on geospatial visual foundation models. Leveraging this framework, we examine the urban infrastructure information in 1,178 cities worldwide, covering 93, 088 km2 areas. Cross-validation reveals that the overall accuracy of identified infrastructure achieves 67.0%. It sheds light on the sustainable development of cities and exposes the stark inequity in urban infrastructure provision for vulnerable populations. The identified urban infrastructure dataset of this study are available at https://***/tsinghua-fib-lab/GUI, and the interactive web application is at https://***/yz7xbfy3. © 2024 Copyright held by the owner/author(s).

关键词： Artificial Intelligence

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MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization for Enhanced Continual Learning

MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpne...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xingyu Li Bo Tang Department of Computer Science Tulane University New Orleans United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester United States

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

关键词： Continuing education Training Accuracy Perturbation methods Buildings Artificial neural networks Benchmark testing

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ASDCLAIMS: Twitter Dataset of Claims on Autism Spectrum Disorder

ASDCLAIMS: Twitter Dataset of Claims on Autism Spectrum Diso...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Kim, Michelle YoungJin Kim, Junghwan Kim, Bryan Woosung Johnson, Kristen Marie Kim, Jee-In Michigan State University Computer Science and Engineering East Lansing United States University of Michigan Computer Science and Engineering Ann Arbor United States Rensselaer Polytechnic Institute and MeDKit Inc. Critical Game Design Troy United States Konkuk University and MeDKit Inc. Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9781665480451

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder found in 2.3% of children. Despite its prevalence, fraudulent science and information on autism are pervasive and entrenched in our society. To comprehend and analyze misinformation on autism, we provide the ASDCLAIMS dataset, the first dataset of claims on ASD with topic labels. The claims on Twitter during the week of World Autism Awareness Day are collected using keywords and hashtags pertinent to autism. They are labeled with one of the nine topics, defined based on a keyword extraction method. The dataset and source codes are available at https://***/cozymichelle/AsdClaims. © 2022 IEEE.

关键词： Diseases

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Real-Time Joint Simulation of LiDAR Perception and Motion Planning for Automated Driving

arXiv

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arXiv 2023年

作者： Huang, Zhanhong Zhang, Xiao Huang, Xinming The Department of Electrical & Computer Engineering Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

Real-time perception and motion planning are two crucial tasks for autonomous driving. While there are many research works focused on improving the performance of perception and motion planning individually, it is still not clear how a perception error may adversely impact the motion planning results. In this work, we propose a joint simulation framework with LiDAR-based perception and motion planning for real-time automated driving. Taking the sensor input from the CARLA simulator with additive noise, a LiDAR perception system is designed to detect and track all surrounding vehicles and to provide precise orientation and velocity information. Next, we introduce a new collision bound representation that relaxes the communication cost between the perception module and the motion planner. A novel collision checking algorithm is implemented using line intersection checking that is more efficient for long distance range in comparing to the traditional method of occupancy grid. We evaluate the joint simulation framework in CARLA for urban driving scenarios. Experiments show that our proposed automated driving system can execute at 25 Hz, which meets the real-time requirement. The LiDAR perception system has high accuracy within 20 meters when evaluated with the ground truth. The motion planning results in consistent safe distance keeping when tested in CARLA urban driving scenarios. © 2023, CC BY.

关键词： Motion planning

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Machine Learning-Based Autonomous Framework for Product Classification Over Cloud 1

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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Motwani, Anand Bajaj, Goldi Arya, Monika Sar, Sumit Kumar Manoj, S. Oswalt School of Computing Science and Engineering VIT Bhopal University MP Sehore India Department of CSE Sardar Vallabhbhai Polytechnic College MP Bhopal India Department of CSE Bhilai Institute of Technology CG Durg India Department of Computer Science and Business Sri Krishna College of Engineering Coimbatore India

ISBN: (数字)9789811996382

ISBN: (纸本)9789811996375

Electronic commerce (EC) companies have a comprehensive database of products, customers, their transactions and purchase patterns. Among these activities, measuring user interactions with various product categories on e-commerce Web sites across the user's shopping experience is of utmost importance. Therefore, it is important to apply machine learning (ML) to identify a product category on the basis of its characteristics. At the same time, applying popular ML algorithms to massive datasets brought fresh confronts for the ML practitioners as conventional ML repositories do not handle processing over large datasets and scaling. Therefore, the cloud computing (CC) paradigm has become a valuable option for accelerating ML platforms and solving the issue of big data handling and computation with minimal effort. The work first discusses the data mining (DM), ML and significance of predictive analytics based on ML in the e-commerce domain. Then, the review of multiclass classification and various mathematical models is presented. Some of the e-commerce and relevant classification frameworks are briefly surveyed then. Finally, this work proposes the autonomous framework for product classification, implemented over Microsoft Azure Cloud. The proposed framework predicts the category of the product in the huge product dataset released by a well-known e-commerce company to compete. The dataset is large, making it difficult to process the same on a single machine. So, the classifier is built to take advantage of the scalability of the CC platform. The proposed classifier is built using Optimized ‘Multiclass Logistic Regression’ (O-MLR) by selecting the relevant features and tuning the hyper-parameters. Finally, the experiment is deployed over a leading ML workspace over a cloud environment. The suggested model’s outcomes are compared and assessed in terms of overall classification accuracy and class-based accuracy. The findings of this study are promising, and the work also po

关键词： Predictive analytics

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Comparison of SQL and NoSQL databases with different workloads: MongoDB vs MySQL evaluation

Comparison of SQL and NoSQL databases with different workloa...

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2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022

作者： Capris, Ticiana Melo, Pedro Garcia, Nuno M. Pires, Ivan Miguel Zdravevski, Eftim Polytechnic Institute of Viseu Computer Science Department Viseu Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal University Ss Cyril and Methodius Faculty of Computer Science and Engineering Skopje Macedonia

ISBN: (纸本)9781665490580

One of the most important considerations when selecting a database is how relational (SQL) and non-relational (NoSQL) data structures will interact. While all options are viable, consumers should take certain distinctions into account before choosing. Since SQL databases are vertically scalable, you can typically scale server components like CPU, RAM, or SSD. NoSQL databases, on the other hand, support horizontal scaling. As a result, you can increase the capacity of your NoSQL database by fragmenting (data partitioning), or by adding extra servers. Why, then, is it still challenging to choose the instance that is most appropriate for a given application and requires the least amount of runtime? Because data that will be conveyed via the internet uses a cloud in computer networks as a metaphor. To determine which model to utilize, it is required to conduct a comparison study of SQL-oriented database engines. SQL has a form created for another side of non-productive data and is offered in the form of ordered data, but NoSQL databases are horizontally expandable. Workload management solutions are therefore also in charge of automating organizational procedures, i.e., they carry out activities without requiring manual employee attendance. For businesses trying to implement continuous delivery methods and enhance the effectiveness of customer service delivery, they are unavoidable. © 2022 IEEE.

关键词： Big data

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Monitoring Electric Vehicles on The Go 19

Monitoring Electric Vehicles on The Go

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Aguiari, Davide Chou, Ka Seng Tse, Rita Pau, Giovanni Alma Mater Studiorum - University of Bologna Department of Computer Science and Engineering IT Bologna Italy Macao Polytechnic Institute School of Applied Sciences China University of California Ucla Samueli Computer Science Los Angeles United States

ISBN: (纸本)9781665431620

Electric vehicles (EV) feature detailed monitoring and control over the CAN bus. Some of this data is made available to users on the On-Board Diagnostic version II (OBDII) bus thus providing an opportunity for large scale high-frequency data collection. This paper introduces a connected monitoring system for OBDII equipped vehicles. The system comprises a low cost hardware design and monitoring algorithms designed to optimize the number of variables collected and their collection frequency. The algorithm aims at collecting a high quantity of Battery Management System (BMS) data in electric vehicles together with power-usage data to enable short and long term estimation for battery state of health (SOH) and state of charge (SOC). The proposed system has been implemented and tested on a Nissan Leaf and lead to the acquisition of 1.7 million records over 120 hours of driving. © 2022 IEEE.

关键词： Electric vehicles

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Longitudinal Analysis of Mask and No-Mask on Child Face Recognition 22

Longitudinal Analysis of Mask and No-Mask on Child Face Reco...

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Proceedings of the Thirteenth Indian Conference on computer Vision, Graphics and Image Processing

作者： Praveen Kumar Chandaliya Zahid Akhtar Neeta Nain Computer Science and Engineering The LNM Institute of Information Technology Jaipur IN and Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Network and Computer Security College State University of New York Polytechnic Institute US Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

ISBN: (纸本)9781450398220

Face is one of the most widely employed traits for person recognition, even in many large-scale applications. Despite technological advancements in face recognition systems, they still face obstacles caused by pose, expression, occlusion, and aging variations. Owing to the COVID-19 pandemic, contactless identity verification has become exceedingly vital. To constrain the pandemic, people have started using face mask. Recently, few studies have been conducted on the effect of face mask on adult face recognition systems. However, the impact of aging with face mask on child subject recognition has not been adequately explored. Thus, the main objective of this study is analyzing the child longitudinal impact together with face mask and other covariates on face recognition systems. Specifically, we performed a comparative investigation of three top performing publicly available face matchers and a post-COVID-19 commercial-off-the-shelf (COTS) system under child cross-age verification and identification settings using our generated synthetic mask and no-mask samples. Furthermore, we investigated the longitudinal consequence of eyeglasses with mask and no-mask. The study exploited no-mask longitudinal child face dataset (i.e., extended Indian Child Longitudinal Face Dataset) that contains 26,258 face images of 7,473 subjects in the age group of [2, 18] over an average time span of 3.35 years. Due to the combined effects of face mask and face aging, the FaceNet, PFE, ArcFace, and COTS face verification system accuracies decrease by approximately, and, respectively.

关键词： Child Face Recognition

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Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption

arXiv

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arXiv 2024年

作者： Muhammed, Rebwar Khalid Aziz, Ribwar Rashid Hassan, Alla Ahmad Aladdin, Aso Mohammed Saydah, Shaida Jumaah Rashid, Tarik Ahmed Hassanc, Bryar Ahmad Network Department Computer Science Institute Sulaimani Polytechnic University Sulaymaniyah Iraq Database Department Computer Science Institute Sulaimani Polytechnic University Sulaymaniyah Iraq Department of Computer Science College of Science Charmo University Sulaymaniyah Iraq Ministry of Education Kirkuk Education Department of Kurdish Studies Hawazen Preparatory School for Girls Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq

Nowadays, cybersecurity has grown into a more significant and difficult scientific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guarantees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The assessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements. © 2024, CC BY.

关键词： Cybersecurity

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