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6th IEEE International Conference on Computing, Communication and Automation, ICCCA 2021

作者： Jayagoda, N.M. Jayawardana, O.R. Welivita, W.W.T.P. Weerasinghe, L. Dassanayake, T. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665414739

Not so long ago, online shopping for groceries, electronics, and furniture items seemed futuristic. But today, it has become a norm to order requisites through online platforms using smart devices and deliver them to customers' doorstep. With the emerge of technologies such as artificial intelligence, machine learning, deep learning, augmented reality, retail becomes progressively effortless. One such emerging futuristic technology involved recently in online shopping is Augmented Reality (AR) which is rapidly adopted by many industries. In multi-story supercenters, also known as 'Hypermarkets', the customer often feels lost due to difficulty in finding exactly what they looking for, and also in conventional online shopping, often customers are in two minds whether to purchase an item or not since it lacks the proper visualization, touch, and feel of the product. In this research study, we propose a mobile-based solution with augmented reality, which assists the customer when shopping in-store as well as when shopping online to mitigate the difficulties and hesitancies faced while shopping. The results are commendable with 96.21 % accuracy in suggesting visually similar items and 89.59% accuracy in detecting emotional implications of product reviews. © 2021 IEEE.

关键词： Sales

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Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

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A resource conscious human action recognition framework using 26-layered deep convolutional neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第28-29期80卷 35827-35849页

作者： Khan, Muhammad Attique Zhang, Yu-Dong Khan, Sajid Ali Attique, Muhammad Rehman, Amjad Seo, Sanghyun Department of Computer Science HITEC University Taxila Pakistan Department of Informatics University of Leicester Leicester UK Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Department of Software Sejong University Seoul South Korea Department of Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Art College of Art and Technology Chung-Ang University Anseong Republic of Korea

Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and global points are requires known as features. These features modified during the variation in human movement. But due to a bit change in several human actions, the features of these actions are mixed that degrade the recognition performance. In this article, we design a new 26-layered Convolutional Neural Network (CNN) architecture for accurate complex action recognition. The features are extracted from the global average pooling layer and fully connected (FC) layer, and fused by a proposed high entropy-based approach. Further, we propose a feature selection method name Poisson distribution along with Univariate Measures (PDaUM). Few of fused CNN features are irrelevant, and few of them are redundant that makes the incorrect prediction among complex human actions. Therefore, the proposed PDaUM based approach selects only the strongest features that later passed to the Extreme Learning Machine (ELM) and Softmax for final recognition. Four datasets are using for experimental analysis - HMDB51 (51 classes), UCF Sports (10 classes), KTH (6 classes), and Weizmann (10 classes). On these datasets, the ELM classifier gives an improved performance as compared to a Softmax classifier. The achieved accuracy on each dataset is 81.4%, 99.2%, 98.3%, and 98.7%, respectively. Comparison with existing techniques, it is shown that the proposed architecture gives better performance in terms of accuracy and testing time.

关键词： Action recognition CNN architecture Features fusion Features selection ELM

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IoT Based Smart Systems using Machine Learning (ML) and Artificial Intelligence (AI): Vulnerabilities and Intelligent Solutions 13

IoT Based Smart Systems using Machine Learning (ML) and Arti...

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13th International Conference on Society and Information Technologies, ICSIT 2022

作者： Ahmed, Shaftab Ilyas, Mohammad Raja, M. Yasin Akhtar Software Engineering Department Bahria University Islamabad Pakistan College of Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Center for Optoelectronics & Optical Communications University of North Carolina Charlotte NC United States

ISBN: (纸本)9781950492633

Internet of Things (IoT) has assumed great importance in technical and social domains due to desire of smart living and intelligent solutions for industrial operations, home automation and healthcare. The telecommunication networks provide all-time internet connectivity for the devices in physical systems and hand- held devices. The developments have made it easy to remain engaged on all time, anywhere basis, while users interact with one or more applications. Many smart devices may interact in the background resulting in event-driven intelligent activities raising alerts or recording status summary under a policy. The smart solutions are being shaped for the industry, transport, eHealthcare, eEducation and other daily life activities. IoT activities are autonomous and support dynamic Machine-to-Machine (M2M) communication. The challenges of hetrogeneity, dynamic variation in signal quality and large volume of data are being addressed through number of techniques. In this paper, we discuss IoT based smart system technologies, security, vulnerabilities and role of intelligent solutions using Machine Learning (ML) and Artificial Intelligence (AI). A crucial factor hindering the ongoing efforts for widespread IoT-adoption, is security. We propose the requirement of Standard Security Framework (SSF) for platform independent and interoperable hardware/software modules in distributed networks domains. Trends to use Fog- and Edge-computing along with cloud applications has been also reviewed in the context to security and efficiency. © 2022 by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Internet of things

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A Systematic Review of Approaches for Reviewing Security-Related Aspects in Agile Requirements Specification of Web Applications

A Systematic Review of Approaches for Reviewing Security-Rel...

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computer Science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Aqsa Munir Mohammed Assiri Saba Naveed Alam Muzna Khan Wasi Haider Butt Mamoona Humayun Department of Computer and Software Engineering College of Electrical and Mechanical Engineering (EME) National University of Science and Technology (NUST) Islamabad Pakistan Department of Computer Science College of Sciences and Humanities Aflaj Prince Sattam bin Abdulaziz University Saudi Arabia Department of Software Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Al-Jouf Saudi Arabia

Imperfections in requirement specification can cause serious issues during software development life cycle. It might bring about inferior quality products due to missing attributes, for example, security. Specifically, Web Applications are considered as obvious target for getting significant information. Security aspects have gotten hard to manage in web applications because security requirements are not regularly seen appropriately and often details are missing which lead into ill-characterized security-related aspects. With the help of systemic literature review (SLR), we have identified 32 major research works which were published in period of year 2010 to 2020. We identified 38 security-related aspects of web applications and also 13 techniques and tools for security related aspects in agile requirement specification. We have also analyzed 22 challenges in reviewing security related aspects in agile development life cycle.

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Enhancing the Robustness of QMIX against State-adversarial Attacks

arXiv

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arXiv 2023年

作者： Guo, Weiran Liu, Guanjun Zhou, Ziyuan Wang, Ling Wang, Jiacun Department of Computer Sciences Tongji University Shanghai China College of Transportation Engineering Tongji University Shanghai China Department of Computer Science and Software Engineering Monmouth University West Long Branch United States

Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent’s observation. Most recent research has concentrated on robust single-agent reinforcement learning (SARL) algorithms against state-adversarial attacks. Still, there has yet to be much work on robust multi-agent reinforcement learning. Using QMIX, one of the popular cooperative multi-agent reinforcement algorithms, as an example, we discuss four techniques to improve the robustness of SARL algorithms and extend them to multi-agent scenarios. To increase the robustness of multi-agent reinforcement learning (MARL) algorithms, we train models using a variety of attacks in this research. We then test the models taught using the other attacks by subjecting them to the corresponding attacks throughout the training phase. In this way, we organize and summarize techniques for enhancing robustness when used with MARL. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Effect of Enhanced Neurofeedback Stimulus Content on Frontal Alpha Asymmetry 5

Effect of Enhanced Neurofeedback Stimulus Content on Frontal...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Ali, Syed Saad Azhar Hafeez, Yasir Ariffin, Tajol Ashraf Bin Tajol Adil, Syed Hasan Amin, Hafeez Ullah Boon, Tang Tong Universiti Teknologi Petronas Centre for Intelligent Signal and Imaging Research Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia Universiti Teknologi Petronas Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia Iqra University Software Engineering Department Karachi Pakistan

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Neurofeedback training has various therapeutic effects to mitigate the mental illness such as stress, anxiety, and depression. Neurofeedback is a non-invasive self-training of brain with the help of auditory or visual input stimulus. The enhancement to neurofeedback stimulus content can help to improve the efficacy of neurofeedback training. Therefore, in this study, the game stimulus content is enhanced to find the effect of enhanced parameters towards the efficacy of neurofeedback. The enhanced parameters include the changing of background, colour contrast, and background music. The neurofeedback training sessions are performed to mitigate the stress among university students. The electroencephalogram (EEG) data is recorded and feedback from the participants are also taken. The graphical and statistical analysis of the results of the neurofeedback training using alpha asymmetry show the effectiveness of neurofeedback towards mitigation of stress. © 2022 IEEE.

关键词： Diseases

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Fpa-GCN: enhancing aspect sentiment triplet extraction with feature-rich prediction-aware graph convolutional networks

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Applied Intelligence 2025年第9期55卷 1-29页

作者： Jiang, Haoyu Chen, Xiaoliang Miao, Duoqian Zhang, Hongyun Qin, Xiaolin Gu, Xu Lu, Peng Li, Xianyong School of Computer and Software Engineering Xihua University Chengdu P. R. China College of Electronic and Information Engineering Tongji University Shanghai P. R. China Department of Computer Science and Operations Research University of Montreal Montreal Canada Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu P. R. China

Aspect Sentiment Triplet Extraction (ASTE) is a critical yet challenging task within Aspect-Based Sentiment Analysis (ABSA), which is dedicated to discerning aspect terms, their corresponding opinion terms, and sentiment polarities. The applicability of ASTE spans a broad array of sectors, from e-commerce to social media analytics and customer feedback analysis, highlighting its versatility and significance across various industries. However, the prevailing research methodologies frequently fail to capture the slight interplay among the three essential sentiment elements, often resulting in a misalignment that hampers the establishment of accurate contextual relationships. These misalignments often occur as discrepancies in the model’s understanding of contextually related content, deviating from expected associations. To address these challenges, this paper introduces the Feature-Rich Prediction-Aware Graph Convolutional Network (Fpa-GCN) model, meticulously designed for the ASTE task. By harnessing the power of BERT for superior semantic feature extraction and utilizing these features through Biaffine attention, the Fpa-GCN model employs a multi-branch graph convolutional network to effectively encapsulate contextual relations. Additionally, the model incorporates Information Fusion and an advanced Gating mechanism, further enhancing triplet extraction precision. Rigorous evaluations on benchmark datasets affirm the Fpa-GCN model’s supremacy over existing state-of-the-art models, achieving significant F1 score improvements. Specifically, the model achieves gains of 1.31%, 0.11%, 3.7%, and 2.15% on the Restaurant14, Laptop14, Restaurant15, and Restaurant16 datasets, respectively, in experiment group $$ \varvec{D_{1}} $$ , and 1.66%, 0.08%, 4%, and 3.33% on the same datasets in experiment group $$ \varvec{D_{2}} $$ . These results underscore the Fpa-GCN model’s efficacy and its potential to set a new benchmark in the field of ABSA.

关键词： Natural language processing Aspect-based sentiment analysis Aspect sentiment triplet extraction Graph convolutional network Biaffine attention

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CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation

arXiv

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arXiv 2023年

作者： Truong, Thanh-Dat Duong, Chi Nhan Dowling, Ashley Phung, Son Lam Cothren, Jackson Luu, Khoa Department of Computer Science and Computer Engineering University of Arkansas United States Department of Computer Science and Software Engineering Concordia University Canada Department of Entomology and Plant Pathology University of Arkansas United States Faculty of Engineering and Information Sciences University of Wollongong Australia Department of Geosciences University of Arkansas United States

Understanding semantic scene segmentation of urban scenes captured from the Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a perception model for UAV. With the limitations of large-scale densely labeled data, semantic scene segmentation for UAV views requires a broad understanding of an object from both its top and side views. Adapting from well-annotated autonomous driving data to unlabeled UAV data is challenging due to the cross-view differences between the two data types. Our work proposes a novel Cross-View Adaptation (CROVIA) approach to effectively adapt the knowledge learned from on-road vehicle views to UAV views. First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views. Second, cross-view correlations from image space are effectively transferred to segmentation space without any requirement of paired on-road and UAV view data via a new Geometry-Constraint Cross-View (GeiCo) loss. Third, the multi-modal bijective networks are introduced to enforce the global structural modeling across views. Experimental results on new cross-view adaptation benchmarks introduced in this work, i.e., SYNTHIA → UAVID and GTA5 → UAVID, show the State-of-the-Art (SOTA) performance of our approach over prior adaptation methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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