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A learning artificial visual system for motion direction detection

Neural Computing and Applications 2024年第27期36卷 17181-17197页

作者： Chen, Tianqi Kobayashi, Yuki Todo, Yuki Tang, Zheng Faculty of Electrical and Computer Engineering Kanazawa University Kakuma Ishikawa Kanazawa9201162 Japan Department of Intelligence Information Systems University of Toyama Toyama9308555 Japan

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neurons

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Enhanced Metaheuristics with Machine Learning Enabled Cyberattack Detection Model

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Intelligent Automation & Soft Computing 2023年第9期37卷 2849-2863页

作者： Ahmed S.Almasoud Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia

The Internet of Things(IoT)is considered the next-gen connection network and is ubiquitous since it is based on the *** Detection System(IDS)determines the intrusion performance of terminal equipment and IoT communication procedures from IoT environments after taking equivalent defence measures based on the identified *** this back-ground,the current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Detection and Classification(EMML-CADC)model in an IoT *** aim of the presented EMML-CADC model is to detect cyberattacks in IoT environments with enhanced *** attain this,the EMML-CADC model primarily employs a data preprocessing stage to normalize the data into a uniform *** addition,Enhanced Cat Swarm Optimization based Feature Selection(ECSO-FS)approach is followed to choose the optimal feature ***,Mayfly Optimization(MFO)with Twin Support Vector Machine(TSVM),called the MFO-TSVM model,is utilized for the detection and classification of ***,the MFO model has been exploited to fine-tune the TSVM variables for enhanced *** performance of the proposed EMML-CADC model was validated using a benchmark dataset,and the results were inspected under several *** comparative study concluded that the EMML-CADC model is superior to other models under different measures.

关键词： Metaheuristics cyberattack detection machine learning cat swarm optimization security

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D-CIDE: An Interactive Code Learning Program 15

D-CIDE: An Interactive Code Learning Program

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15th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2024

作者： Grant, Lukas Tennyson, Matthew F. Owen, Jason Computer Science and Information Systems Murray State University MurrayKY42071 United States

ISBN: (纸本)9781950492787

This paper introduces D-CIDE (Distributed Classroom Integrated Development Environment), a tool that is designed to improve student-teacher interactions in programming classes. D-CIDE’s main objective is to provide more meaningful interactions between teachers and students. Its goal is to create a more seamless and interactive learning environment for everyone who uses it. D-CIDE is a distributed integrated development environment (IDE), where the teacher (host) can manage and interact with the IDEs of all students (clients). It makes use of server-client interactions to allow live sharing and editing of code, making it a useful tool for demonstrating coding techniques and quickly addressing student questions. The front-end was developed using HTML, CSS, and JavaScript, and provides a way for the students and teachers to interact with each other. The back-end is made with JavaScript and NodeJS and handles data processing and transmission. The effectiveness of D-CIDE was analyzed through a classroom case study involving a small group of students. The study measured students' engagement, enjoyment, and learning outcomes using D-CIDE compared to traditional teaching methods. Results indicated an increase in student engagement and satisfaction when D-CIDE was used, as well as an improvement in students' learning experiences. © IMCIC 2024. All rights reserved.

关键词： Curricula

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POA-Net: Dance Poses and Activity Classification Using Convolutional Neural Networks

POA-Net: Dance Poses and Activity Classification Using Convo...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Agarwal, Aditya Jha, Parth Jain, Ojas Bera, Asish Bits Pilani Department of Computer Science and Information Systems Pilani campus Rajasthan 333031 India

ISBN: (纸本)9798350364866

Dance poses represent a complex human body-part movement, and express emotions and gesture. Dance pose classification is a challenging problem in computer vision. Convolutional Neural Networks (CNNs) have witnessed significant performance improvements in recognizing dance poses from images and videos. Most of the dance datasets in existing works are video-based and are not available publicly. This work contributes an image dataset representing 8 new dance styles blended with the Indian and international dance themes, called Dance-8. These unique 8 dance styles are combined with the Dance-12 public dataset for improving the posture diversity and dataset size. This extended dataset is called Dance-20. A custom CNN is developed for dance POses and Activity classification, named POA-Net. All three dance datasets have been evaluated using standard base CNNs and POA-Net. The POA-Net has attained an accuracy of 73.27% on Dance-8, 82.10% on Dance-12, and 73.10% on Dance-20. These performances are better than those of standard backbones, such as VGG16 and Inception-V3. The best accuracy of 81.57%, 85.08% and 76.73% has been achieved by MobileNet-v2 on these Dance-8, 12, and 20 datasets, respectively. Moreover, POA-Net has achieved the state-of-the-art accuracy of 99.74% on the DIAT, which is a radar-based human action image dataset. Dance-8 and Dance-20 datasets are available at: https://***/datasets/m6syhcn2nh/1. © 2024 IEEE.

关键词： Convolutional Neural Networks (CNNs) Dance posture recognition Human action classification

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Enhancing Financial Sector Cybersecurity: A Multi-Component Framework for Supply Chain Risk Mitigation 6

Enhancing Financial Sector Cybersecurity: A Multi-Component ...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Kiran, Suryaa Praveen, Yomal Silva, Akila Bagya, Yasith Yapa, Kavinga Siriwardana, Deemantha Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Supply chains are being used at scale within new industries, mainly in the financial sector and the cyber security risks emerging in this area have been on the rise. Given recent attack scenarios the mitigation strategies taken within these areas are limited. This research paper focuses on the main areas where the risk can be reduced to provide an overall effective solution to reduce the risks. However, there are certain challenges in minimizing the risks associated, such as third-party vendor identification, classifying and securing data, and gathering relevant external threat information. This research utilizes a centralized risk assessment gateway to assess the security level of each third-party vendor. Further, there will be a secure transfer of text and image data once the vendors have been assessed and these will be done in correlation with a zero-trust policy engine where data classification occurs based on unique trust levels. In addition, it will also use machine learning to tailor the external cyber threat feeds to provide valuable insights regarding threat attacks, vulnerabilities, and patches within the sector to gain more granular intelligence and reduce analysis time. The overall solution will serve as a zero-trust approach to mitigate the attacks within the financial fields of the supply chain sector. © 2024 IEEE.

关键词： data classification machine learning risk assessment supply chains zero trust policy engine

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Improving Dementia Prediction Using Ensemble Majority Voting Classifier

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Annals of Data Science 2024年 1-21页

作者： Niyas, K. P. Muhammed Thiyagarajan, P. Department of Information Systems and Business Analytics Rajagiri Business School Kochi Ernakulam India Department of Computer Science Central University of Tamil Nadu Thiruvarur India

Early detection of dementia patients in advance is a great concern for the physicians. That is why physicians make use of multi modal data to accomplish this. The baseline visit data of the patients are mainly utilized for this task. Modern Machine Learning techniques provide empirical evidence based approach to physicians for predicting the diagnosis status of the patients. This paper proposes an ensemble majority voting classifier approach for improving the detection of dementia using baseline visit data. The ensemble model consists of Logistic Regression, Random Forest, and Naive Bayes Classifiers. The proposed ensemble classifier reported with a BCA, F1-score of 92%, 0.92 for classifying demented and non-demented patients. Our results suggest that the prediction using the ensemble majority voting classifier improves the Balanced Classification Accuracy, F1-score for predicting dementia on the multi modal data of Open Access Series Imaging Dataset. The results using ensemble models are promising and highlight the importance of using ensemble models for dementia detection using multimodal data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Forecasting

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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT 21

Empowering Low-Resource Language Translation: Methodologies ...

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21st International Conference on Spoken Language Translation, IWSLT 2024

作者： Anand, Harpreet Singh Dash, Amulya Ratna Sharma, Yashvardhan Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798891761414

This paper presents the methodologies implemented for the Automatic Speech Recognition and Machine Translation for the language pairs Bhojpuri-Hindi and Marathi-Hindi for the Dialectal and Low-Resource shared task proposed by The International Conference on Spoken Language Translation (IWSLT) for 2024. The implemented method uses the transcriptions generated through a fine-tuned Whisper models(for Marathi-Hindi) and vakyansh-wav2vec model (for Bhojpuri-Hindi) and generates the translations using fine-tuned NLLB(No Language Left Behind) Models for both the tasks. The selection of more accurate translation is done through sentence-embeddings generated using the MuRIL(Multilingual Representations for Indian Languages)(Khanuja et al., 2021) model for the Marathi-Hindi task. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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An Algorithmic Approach to Analyzing Rhetorical Structures 5

An Algorithmic Approach to Analyzing Rhetorical Structures

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5th Workshop on Computational Approaches to Discourse, CODI 2024

作者： Potter, Andrew Computer Science & Information Systems Department University of North Alabama FlorenceAL United States

ISBN: (纸本)9798891760790

Although diagrams are fundamental to Rhetorical Structure Theory, their interpretation has received little in-depth exploration. This paper presents an algorithmic approach to accessing the meaning of these diagrams. Three algorithms are presented. The first of these, called Reenactment, recreates the abstract process whereby structures are created, following the dynamic of coherence development, starting from simple relational propositions, and combing these to form complex expressions which are in turn integrated to define the comprehensive discourse organization. The second algorithm, called Composition, implements Marcu’s strong nuclearity assumption. It uses a simple inference mechanism to demonstrate the reducibility of complex structures to simple relational propositions. The third algorithm, called Compression, picks up where Marcu’s assumption leaves off, providing a generalized fully scalable procedure for progressive reduction of relational propositions to their simplest accessible forms. These inferred reductions may then be recycled to produce RST diagrams of abridged texts. The algorithms described here are useful in positioning computational descriptions of rhetorical structures as discursive processes, allowing researchers to go beyond static diagrams and look into their formative and interpretative significance. © 2024 Association for Computational Linguistics.

关键词： Graphic methods

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Data Augmentation Impact on Deep learning Performance for Stress Detection 11

Data Augmentation Impact on Deep learning Performance for St...

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11th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2023

作者： Hamdi, Eman Mabrouk, Mohamed Rady, Sherine Aref, Mostafa Faculty of Computer and Information Science Computer Science Department Cairo Egypt Faculty of Computer and Information Science Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

The mental health of individuals has a major influence on society. Mental disorders such as depression and anxiety are related to issues and distress to function in work, social, or family gatherings. Motivated by helping people to early detect stress, we propose a deep learning model for stress detection that is trained and tested on Stress Annotated Dataset (SAD). Also, different techniques of textual data augmentation are applied to generate more data samples to study the impact on the stress detection task. The model achieves competitive results in the task of stress detection and proves that data augmentation has a great effect on the performance. As the accuracy is raised by 6.1% to 10.4 % by applying various techniques of data augmentation. © 2023 IEEE.

关键词： Stresses

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ComFaaS Distributed: Edge Computing with Function-as-a-Service in Parallel Cloud Environments 7

ComFaaS Distributed: Edge Computing with Function-as-a-Servi...

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7th International Conference on information and computer Technologies, ICICT 2024

作者： Lee, Jaden Jinu Nava, Judah Lee, Hanku Minnesota State University Moorhead Computer Science And Information Systems MoorheadMN United States

ISBN: (纸本)9798350385625

Function-as-a-Service (FaaS) has emerged as a revolutionary service platform, abstracting the complexities of hardware, operating systems, and web hosting services. This allows developers to focus solely on implementing their service applications, making FaaS an ideal platform for the scalable manipulation of large data sets. Traditionally deployed on the cloud, FaaS now faces a new frontier: the network edge. Leveraging the edge offers several potential benefits, including reduced latency and improved resource utilization, making it a promising approach for efficient FaaS deployment. As the daily volume and complexity of data we handle continues to grow, adopting a parallel computing paradigm has become increasingly important to ensure fast and efficient execution of computational tasks. Addressing this need, ComFaaS Distributed embarks on a comprehensive comparison of the capabilities of parallelized edge and cloud environments for FaaS deployment. Utilizing benchmark programs meticulously crafted to simulate event-triggered scenarios, ComFaaS Distributed aims to provide valuable insights into the performance and potential of FaaS at the edge, paving the way for a future where parallel computing empowers the efficient and scalable processing of ever-growing data volumes. © 2024 IEEE.

关键词： Edge computing

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