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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-dept. overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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A catalogue of game-specific anti-patterns 2022

A catalogue of game-specific anti-patterns

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15th Innovations in Software Engineering Conference, ISEC 2022

作者： Agrahari, Vartika Chimalakonda, Sridhar Lab Dept. of Computer Science and Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9781450396189

With the ever-increasing use of games, game developers are expected to write efficient code and support several aspects such as security, maintainability, and performance. However, the continuous need to update the features of games in shorter duration might compel the developers to use anti-patterns, code smells and quick-fix solutions that may affect the functional and non-functional requirements of the game. These bad practices may lead to technical debt, poor program comprehension, and can cause several issues during software maintenance. While there exists empirical research on games, we are not aware of any work on understanding and cataloguing these anti-patterns in the domain of games. Thus, in this paper, we propose a catalogue of game-specific anti-patterns by mining and analyzing 892 commits, 189 issues, and 104 pull requests from 100 open-source GitHub game repositories. We use regular expressions and thematic analysis on this dataset and present a catalogue of ten game-specific anti-patterns along with examples. We see this catalogue as a first step for further empirical research in the domain of games and can help game developers to improve quality of games. © 2022 ACM.

关键词： Open source software

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Universal Simulation Platform Online, A Multi-Participants I.E.D. Disposal Simulator 11

Universal Simulation Platform Online, A Multi-Participants I...

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Kahrizi, Arman Hamzehlou Chun Ki Chan, Christopher Ferworn, Alexander Toronto Metropolitan University Dept. of Computer Science Toronto Canada Research Centre Innovative Sciences Inc. Taichung Taiwan

ISBN: (纸本)9798350336986

Improvised explosive devices are unconventional weapons that are a significant threat to public safety. Explosive ordnance disposal personnel are tasked with determining methods and procedures for locating, neutralizing, and disposing of chemical, biological, radiological, nuclear and explosive threats in extreme environments. Training methods vary depending on the region and military unit, and may use a combination of tools to facilitate a learning process. In our previous work, we proposed a Virtual Reality simulation called Universal Simulation Platform to enable explosive ordnance disposal personnel to train in a safe virtual environment without being exposed to potentially harmful real-world situations whilst being cost effective. In this paper, we propose a multi-participant revision of the simulation software, which allows multiple participants to connect and join a session, work together and attempt to neutralize a bomb(s). A multi-participant improvised explosive device disposal simulation is a novel virtual reality training approach which introduces crucial neutralization training aspects such as teamwork and communication, often neglected functions of virtual reality training simulations. © 2023 IEEE.

关键词： Virtual reality

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TARG: A Reference-Free, Lossless, Customized General-Purpose Encoder for Genome Sequence in Raw, FASTA, or Multi-FASTA Formats

TARG: A Reference-Free, Lossless, Customized General-Purpose...

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International Conference on computer, Communication, Control and Information Technology (C3IT)

作者： Subhankar Roy Partha Ghosh Anirban Mukhopadhyay Dept. of Computer Science and Engg. Academy of Technology Hooghly India Dept. of Computer Science and Engg. University of Kalyani Nadia India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

The ability to efficiently encode and store vast amounts of genome sequence data is paramount for various analytical and computational tasks. General-purpose compressors fail to efficiently compress genome sequences, primarily due to the highly repetitive nature of genomic data, the complexity and variability of genomes across organisms, the large size of genome sequences, the limitations of standard compression algorithms, and the absence of optimization for genomic data patterns. This article addresses this challenge through the development of a reference-free, lossless, customized general-purpose encoder for genome sequences in raw, FASTA (FAST-ALL), or multi-FASTA formats. The proposed encoder offers a versatile solution that can handle diverse genome datasets without relying on preexisting reference sequences, ensuring compatibility with emerging genome sequencing technologies. We tested and evaluated the encoder thoroughly to show that it works well and is reliable. We looked at compression ratios, compression and decompression speeds, memory, and how well it recreated the original genome sequences. This encoder not only addresses the pressing need for efficient data storage and transmission in genomics but also paves the way for further advancements in genome analysis and interpretation. Unlike other algorithms, the proposed method, TARG (Two-bit coding encoded using ASCII with Run length encoding followed by General-purpose encoding), does not depend on time-consuming parameter settings. Based on experiments, the average compression ratio of TARG on 10 benchmark genome datasets is 4.33, while the current state-of-the-art general-purpose algorithms gzip, bzip2, zstd, and bsc have respective averages of 3.26, 3.51, 4.21, and 4.28.

关键词： Memory management Genomics Transforms Benchmark testing Approximation algorithms Encoding Compressors ASCII Bioinformatics Optimization

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Poly2Vec: Polymorphic Encoding of Geospatial Objects for Spatial Reasoning with Deep Neural Networks

arXiv

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arXiv 2024年

作者： Siampou, Maria Despoina Li, Jialiang Krumm, John Shahabi, Cyrus Lu, Hua University of Southern California Dept. of Computer Science Los AngelesCA United States Roskilde University Dept. of Computer Science Roskilde Denmark

Encoding geospatial data is crucial for enabling machine learning (ML) models to perform tasks that require spatial reasoning, such as identifying the topological relationships between two different geospatial objects. However, existing encoding methods are limited as they are typically customized to handle only specific types of spatial data, which impedes their applicability across different downstream tasks where multiple data types coexist. To address this, we introduce Poly2Vec, an encoding framework that unifies the modeling of different geospatial objects, including 2D points, polylines, and polygons, irrespective of the downstream task. We leverage the power of the 2D Fourier transform to encode useful spatial properties, such as shape and location, from geospatial objects into fixed-length vectors. These vectors are then inputted into neural network models for spatial reasoning tasks. This unified approach eliminates the need to develop and train separate models for each distinct spatial type. We evaluate Poly2Vec on both synthetic and real datasets of mixed geometry types and verify its consistent performance across several downstream spatial reasoning tasks. © 2024, CC BY.

关键词： Deep neural networks

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A Deep Dive into Electra: Transfer Learning for Fine-Grained Text Classification on SST-2 6

A Deep Dive into Electra: Transfer Learning for Fine-Grained...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Fikriansyah, Muhammad Nuha, Hilal Santriaji, Muhammad School of Computing Telkom University Bandung Indonesia University of Central Florida Dept. of Computer Science OrlandoFL United States

ISBN: (纸本)9798350358346

In the dynamic landscape of Natural Language Processing (NLP), a transformative revolution is underway, marked by the rapid evolution and proliferation of pre-Trained language models that have irrevocably reshaped the boundaries of text understanding and classification. Electra, a language model introduced by Clark et al. in 2020, stands out as an innovation with a distinctive pre-Training approach, particularly in fine-grained text classification tasks. This research aims to rigorously evaluate Electra's performance in fine-grained text classification, primarily focusing on sentiment analysis tasks within the SST-2 dataset. Additionally, the study seeks to provide invaluable guidance to researchers and practitioners by elucidating the most effective fine-Tuning strategies and configuration settings. The results highlight the significance of gradual fine-Tuning, with increased layer unfreezing positively impacting model accuracy. This underscores Electra's vast potential for NLP tasks and the importance of thoughtful fine-Tuning processes. © 2023 IEEE.

关键词： Sentiment analysis

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Pain Assessment Model using Facial Recognition 6

Pain Assessment Model using Facial Recognition

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6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Swetha, L. Praiscia, A. Juliet, Sujitha Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665410359

Since pain is a basic piece of life, this study took a gander at the utilization of facial expression to assist individuals with pain. The recognizable proof framework is regularly used to analyze issues and doesn't work and can't be utilized in patients of any age;The aggravation appraisal structure will resolve this issue. Facial monitoring innovation is a significant tool for estimating pain since it is not difficult to utilize and extremely straightforward. Thus, this proposed model utilizes top to bottom exploration to survey facial changes and agony. Pictures in the dataset have been utilized to educate deep learning model in light of the fact that inside and out examinations can assist with illuminating agony. Our strategies can be classifying pain into categories such as: no pain, moderate pain, not tolerable pain and severe pain. Framework execution was surveyed by contrasting the outcomes and those of specialists. As a general rule, our examination has fostered one more strategy for estimating pain before hospitalization, which is straightforward, reasonable, and simple for general society and wellbeing experts to comprehend. Furthermore, this analytic strategy can be utilized for other symptomatic techniques, such as pain detection for infectious diseases. © 2022 IEEE.

关键词： Surveys

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Istaqim: An Assistant Application to Correct Prayer for Arab Muslims 5

Istaqim: An Assistant Application to Correct Prayer for Arab...

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5th National Conference of Saudi computers Colleges, NCCC 2022

作者： Hassan, Huda Aziz Qassas, Halah Abdualrahman Alqarni, Badriah Saad Alghuraibi, Rahaf Ibrahim Alghannam, Khadija Fahad Mirza, Olfat Meraj Umm Al Qura University Dept. Computer Science Makkah Saudi Arabia

ISBN: (纸本)9798350333688

Prayer in Islamic community is the second pillar of Islam, a link between the servant and his Lord, it is an act of worship that Muslims must perform five times a day. There are many postures in the pillars of prayer and its duties that must be performed in a precise manner. However, many Muslims, young and old, do not perform prayer properly due to having learned to pray incorrectly, having no one to personally guide them, or being new to prayer. To address this issue, we propose to develop an Artificial Intelligence assistant work using deep learning to guide worshipers through detecting the wrong postures in their prayers and assessing their mistakes and showing corrections. The contribution of this paper lies in training the YOLOv5 neural network to recognize prayer postures. The results are shown only for the correct prayer positions via the pictures with the percentage of the error of each prayer position. © 2022 IEEE.

关键词： Deep learning

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Real-Time Heart Rate Monitoring via Wi-Fi Signal

Real-Time Heart Rate Monitoring via Wi-Fi Signal

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Solgun, Hasan Ali Ozcan, Alp Pinarer, Ozgun Galatasaray University Grad. School of Science and Engineering Istanbul Turkey Galatasaray University Dept. Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350324457

Due to the health data privacy issues, wearable devices are less useful in the industry and can not reflect their potential power. Besides, wearable health devices bring constraints such as limited energy budget, needs to recharge and impact on daily activities. To recover these problems and fulfill healthcare needs, the recent trends in the literature is to use wireless signal to capture/recognize human activities. When human activity and emotion recognition study fields merging with the machine learning techniques, can offer a innovative systems. In recent years, with the help of wireless sensing mechanisms, this study field have gained more importance. So that, these wireless sensing mechanisms have eliminated the requirement of physical body contact such as sensor devices. Nowadays, wireless sensing technologies are preferred with the easy and cost-effective access to off-the-shelf WiFi-enabled devices. In this research, a human activity and emotion recognition system have proposed with usage of WiFi channel state information(CSI) mechanism that modelled by machine learning algorithms. As a result, with a tolerable error rate, it is possible to distinguish person's heart rate information with a contactless solution via wi-fi signal. © 2023 IEEE.

关键词： BPM calculation channel state information contactless sensing CSI Healthcare IoT wifi sensing wireless signal

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WoFS: A Write-only File System for Privacy-aware Wireless Sensor Networks

WoFS: A Write-only File System for Privacy-aware Wireless Se...

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Conference on Local computer Networks (LCN)

作者： Philipp Jahn Markus Sommer Artur Sterz Jonas Höchst Bernd Freisleben Dept. of Mathematics & Computer Science University of Marburg Germany tRackIT Systems GmbH Cölbe Germany

ISBN: (数字)9798350388008

ISBN: (纸本)9798350388015

Wireless sensor networks (WSNs) can automate data sensing tasks. To ensure redundancy and manage network connectivity issues, a sensing node stores a copy of the gathered data. Since this data may contain sensitive personal or business information, protecting privacy and preventing unauthorized access is crucial. We introduce the Write-only File System (WoFS), a novel encryption system for WSNs that secures data without user interaction, even if a sensor node is stolen. WoFS utilizes either symmetric encryption with volatile keys via a ratchet mechanism or asymmetric encryption. Asymmetric encryption, while slower, allows operation post-reboot, unlike the ratchet-based method. Our experiments show that WoFS achieves write speeds of 200 MB/s or higher, making it suitable for WSN applications. All developed software and artifacts are available under a permissive open-source license.

关键词： Wireless sensor networks File systems Microcontrollers Redundancy Licenses Software Encryption

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