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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Anuar, Siti Nabila Shamsul Narashid, Rohayu Haron Razak, Tajul Rosli Hashim, Suhaila Rahim, Ashnita Bohari, Sharifah Norashikin Universiti Teknologi Mara Perlis Branch College Of Built Environment Arau Campus Perlis Arau02600 Malaysia Universiti Teknologi Mara School Of Computing Science College Of Computing Informatics And Mathematics Shah Alam40450 Malaysia

ISBN: (纸本)9798350382310

Cloud seeding is carried out to increase the amount of precipitation, which is useful during drought, especially in agricultural areas. However, the success rate of cloud seeding operations in Malaysia was not fully achieved due to location and temporal factors. The cumulus clouds at low elevation levels are practical in cloud seeding operations. Nowadays, low-level clouds' location can be detected using satellite remote sensing. Therefore, this study aims to determine the potential areas for cloud seeding operations with remotely sensed low-level cloud distribution. The potential areas were retrieved from the low-level cloud distribution of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) with cloud masking method and single reflective band and mapped with Geographic Information System (GIS) overlay analysis. It was found that the locations were coincidently used in previous cloud seeding operations conducted by the Malaysian Meteorological Department (MET) in 2019 which were above the 0.14 threshold. Thus, the potential areas based on low-level clouds for cloud seeding operations can be detected and mapped using Remote Sensing satellite images. The approach can potentially be utilised by related agencies to identify the appropriate areas for cloud seeding operations practically. © 2024 IEEE.

关键词： Remote sensing

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AutoNLP: A Framework for Automated Model Selection in Natural Language Processing 18

AutoNLP: A Framework for Automated Model Selection in Natura...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Saleem, Shehan Kumarapathirage, Sapna University of Westminster Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo06 Sri Lanka

ISBN: (纸本)9789893347928

Although numerous open-source tools exist for machine learning with tabular data, there is a scarcity of comparable resources tailored specifically for NLP. The lack of transparency in the inner workings of existing AutoNLP tools is a significant obstacle in scientific research. AutoML tools are known for their ability to perform model selection with little human intervention, improving the accuracy and reliability of the results. However, conducting a model space search among pre-trained NLP models can be computationally infeasible, making it challenging to determine the optimal NLP model for a given dataset. This research aims to enhance the performance of NLP model selection. Our approach has resulted in higher accuracy than existing methods on the dataset that was created. In our future work, we plan to benchmark our algorithm against datasets created by other researchers to validate its effectiveness. Additionally, we intend to use the same system to perform model selection among popular large language Transformer models. © 2023 ITMA.

关键词： Machine learning

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Classification of SEMG Signal Based Hand Movements by Using Support Vector Machine 6

Classification of SEMG Signal Based Hand Movements by Using ...

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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Zahra, Syeda Rabail Ismail, Sadia Ali, Muhammad Danish Khan, Muhammad Amir Darus, Mohamad Yusof Mazhar, Tahseen Gomal University Institute of Computing & Information Technology Dera Ismail Khan Pakistan University Islamabad Computer Science Comsats Abbottabad Pakistan Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Shah Alam40450 Malaysia Virtual University of Pakistan Department of Computer Science Pakistan

ISBN: (纸本)9798350353853

This paper explores the application of Surface Electromyography (SEMG) for hand movement classification using machine learning techniques. SEMG signals play a crucial role in sports medicine, rehabilitation, prosthesis control, and medical diagnosis. The study addresses a multi-class problem, extending the dataset with limited prior exploration. Nine features from SEMG signal's time domain are extracted, including Maximum Amplitude, Root Mean Square, Slope Sign Change, Variance, Simple Square Integral, Zero Crossing, Waveform Length, and Willison Amplitude. Linear Discriminant Analysis is employed for dimensionality reduction as a feature selection technique. The proposed algorithm achieves an overall classification accuracy of 72% for the classification of six different hand movements performed by five healthy subjects using Support Vector Machines. This research contributes to the advancement of SEMG-based hand movement classification in various practical applications. © 2024 IEEE.

关键词： Dimensionality reduction

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Analysing Regional Disparities and Shifting Trends in Transportation Carbon Emissions in China 20

Analysing Regional Disparities and Shifting Trends in Transp...

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20th IEEE International Conference on e-Business Engineering, ICEBE 2024

作者： Shen, Zhonghua Huang, Jiaying Wu, Dehao Lu, Xin Guangdong University of Foreign Studies School of Business Guangzhou China Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom Leeds Trinity University School of Computer Science Leeds United Kingdom

ISBN: (纸本)9798350365856

This study examines the regional data in transportation carbon emissions across China and investigates the shifting trends of the carbon emission centroid over time. Using the IPCC (2006) carbon emission calculation formula, emissions data from 30 provinces for the years 2005, 2010, 2015, and 2020 were analyzed using an Exploratory Spatial Data Analysis (ESDA) model. The Economic Centroid Model and standard deviation ellipse were applied to assess the movement of the carbon emission centroid, which was consistently located in Henan Province. Further analysis using the Kaya model identifies the key factors influencing transportation carbon emissions in Henan from 2005 to 2020. The findings offer insights into regional carbon reduction strategies and the challenges in achieving China's dual carbon goals. © 2024 IEEE.

关键词： Low emission

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Distribution Matching for Multi-Task Learning of Classification Tasks: A Large-Scale Study on Faces & Beyond 38

Distribution Matching for Multi-Task Learning of Classificat...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Kollias, Dimitrios Sharmanska, Viktoriia Zafeiriou, Stefanos School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering and Informatics University of Sussex United Kingdom Department of Computing Imperial College London United Kingdom

ISBN: (纸本)1577358872

Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer. To provide sufficient learning support, modern MTL uses annotated data with full, or sufficiently large overlap across tasks, i.e., each input sample is annotated for all, or most of the tasks. However, collecting such annotations is prohibitive in many real applications, and cannot benefit from datasets available for individual tasks. In this work, we challenge this setup and show that MTL can be successful with classification tasks with little, or non-overlapping annotations, or when there is big discrepancy in the size of labeled data per task. We explore task-relatedness for co-annotation and co-training, and propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching. To demonstrate the general applicability of our method, we conducted diverse case studies in the domains of affective computing, face recognition, species recognition, and shopping item classification using nine datasets. Our large-scale study of affective tasks for basic expression recognition and facial action unit detection illustrates that our approach is network agnostic and brings large performance improvements compared to the state-of-the-art in both tasks and across all studied databases. In all case studies, we show that co-training via task-relatedness is advantageous and prevents negative transfer (which occurs when MT model's performance is worse than that of at least one single-task model). Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Classification (of information)

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Parameterized Complexity Classification for Interval Constraints 18

Parameterized Complexity Classification for Interval Constra...

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18th International Symposium on Parameterized and Exact Computation, IPEC 2023

作者： Dabrowski, Konrad K. Jonsson, Peter Ordyniak, Sebastian Osipov, George Pilipczuk, Marcin Sharma, Roohani School of Computing Newcastle University United Kingdom Department of Computer and Information Science Linköping University Sweden School of Computing University of Leeds United Kingdom Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland IT University Copenhagen Denmark Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9783959773058

Constraint satisfaction problems form a nicely behaved class of problems that lends itself to complexity classification results. From the point of view of parameterized complexity, a natural task is to classify the parameterized complexity of MinCSP problems parameterized by the number of unsatisfied constraints. In other words, we ask whether we can delete at most k constraints, where k is the parameter, to get a satisfiable instance. In this work, we take a step towards classifying the parameterized complexity for an important infinite-domain CSP: Allen's interval algebra (IA). This CSP has closed intervals with rational endpoints as domain values and employs a set A of 13 basic comparison relations such as "precedes" or "during" for relating intervals. IA is a highly influential and well-studied formalism within AI and qualitative reasoning that has numerous applications in, for instance, planning, natural language processing and molecular biology. We provide an FPT vs. W[1]-hard dichotomy for MinCSP(Γ) for all Γ ⊆ A. IA is sometimes extended with unions of the relations in A or first-order definable relations over A, but extending our results to these cases would require first solving the parameterized complexity of Directed Symmetric Multicut, which is a notorious open problem. Already in this limited setting, we uncover connections to new variants of graph cut and separation problems. This includes hardness proofs for simultaneous cuts or feedback arc set problems in directed graphs, as well as new tractable cases with algorithms based on the recently introduced flow augmentation technique. Given the intractability of MinCSP(A) in general, we then consider (parameterized) approximation algorithms. We first show that MinCSP(A) cannot be polynomial-time approximated within any constant factor and continue by presenting a factor-2 fpt-approximation algorithm. Once again, this algorithm has its roots in flow augmentation. © 2023 Schloss Dagstuhl- Leibniz-Zentrum f

关键词： Approximation algorithms

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YoNet: A Neural Network for Yoga Pose Classification

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SN Computer science 2023年第2期4卷 198页

作者： Ashraf, Faisal Bin Islam, Muhammad Usama Kabir, Md Rayhan Uddin, Jasim Department of Computer Science and Engineering University of California Riverside CA United States School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA United States Department of Computing Science University of Alberta Edmonton AB Canada Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff Wales United Kingdom

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. © 2023, The Author(s).

关键词： Deep learning Image classification Neural network Pose recognition Yoga pose

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Dogs or Not Dogs? Examining ACI Authors' Reporting on Animal Participants' Willingness to Engage in Research: A Spotlight on Mediated and Contingent Consent 23

Dogs or Not Dogs? Examining ACI Authors' Reporting on Animal...

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Proceedings of the Tenth International Conference on Animal-Computer Interaction

作者： Eli B. McGraw Cristina Bosco Lisa Brey Christena Nippert-Eng Department of Informatics Luddy School of Informatics Computing and Engineering and the Cognitive Science Program Indiana University USA Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University USA

ISBN: (纸本)9798400716560

How do Animal-Computer Interaction (ACI) researchers working with live animal participants assess the animals’ willingness to participate in their research? In this paper we present the results of a systematic literature review designed to answer this question by examining the Proceedings of the ACM International Animal Computer Interaction Conference. From 2016-2022, these proceedings included 38 full papers that reported results from research with live animal participants. We found 1) only 74% or 28/38 of the papers reported how they assessed animal participants’ willingness to engage during their research, 2) the authors of papers focused on species other than dogs had a much higher rate of providing this information than did the authors of dog-based studies (100% or 12/12 non-dog papers v 62% or 16/26 of dog-based papers), 3) most researchers who addressed the issue of an animal participant’s willingness to engage in the research relied on some form of mediated consent, informed by behavioral observation methods, to do so. However, the researchers focused on non-dog species were much more likely than researchers focused on dogs to include elements of contingent consent in their protocols (75% (9/12) of the non-dog studies v 12% (3/26) of the dog-related studies). We argue that providing each other with more details about our research methods and possibly more fully embracing the principles of contingent consent would further ACI researchers’ existing ethical commitment to our animal participants, increase our adherence to standard scientific research practice, and accelerate the continued development of the field of Animal-Computer Interaction.

关键词： Animal-Computer Interaction animal behavior animal well-being animal-centered design animal-centered ethics contingent consent dogs mediated consent multi-species interaction design reporting practices research methods scientific transparency voluntary engagement

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A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第23期17卷 4-19页

作者： Chi, Jing On, Chin Kim Zhang, Haopeng Chai, Soo See Faculty of Computing and Informatics University Malaysia Sabah Sabah Malaysia School Information and Electrical Engineering Hebei University of Engineering Hebei China Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Neural network models

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Content Validity Assessment of Metaverse Characteristics Instrument for High school Teachers' Adoption 10

Content Validity Assessment of Metaverse Characteristics Ins...

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10th International Conference on Smart computing and Communication, ICSCC 2024

作者： Cassandra, Cadelina Masrek, Mohamad Noorman Aman, Fadhilah School of Information Science College of Computing Informatics and Media University Teknologi MARA Selangor Shah Alam Malaysia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

This study's aim is to investigate the content validity of an instrument designed to evaluate the Metaverse characteristic assessment for high school teachers' adoption. The content validity of the instrument was explored based on the perspectives of five experts who were chosen based on their expertise and experiences. The Content Validity use content validity index (I-CVI) and scale content validity index (S-CVI). In the pre-testing process, two contexts were tested: the relevant context and clarity context. In the Relevant context, the mean I-CVI ranged from 0.9 to 1, the S-CVI/UA ranged from 0.5 to 1, and the S-CVI/AVE ranged from 0.9 to 1 for the construct of Interactivity, Persistency and Corporeity, the result shows three items need to be revised. In the Clarity context, the mean I-CVI ranged from 0.95 to 1, the S-CVI/UA ranged from 0.75 to 1, and the S-CVI/AVE ranged from 0.95 to 1 for the construct of Interactivity, Persistency and Corporeity, the result show one items need to be revised. The revised items are also proposed in this study for better instruments. © 2024 IEEE.

关键词： Teaching

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