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Surveying neuro-symbolic approaches for reliable artificial intelligence of things

Journal of Reliable Intelligent Environments 2024年第3期10卷 257-279页

作者： Lu, Zhen Afridi, Imran Kang, Hong Jin Ruchkin, Ivan Zheng, Xi Faculty of Data Science City University of Macau Estrada de Coelho do Amaral 999078 China School of Computing Macquarie University Balaclava Rd SydneyNSW2109 Australia Computer Science University of California Los Angeles 404 Westwood Plaza Los AngelesCA90095 United States Electrical and Computer Engineering University of Florida 1889 Museum Rd GainesvilleFL32611 United States

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT), known as the Artificial Intelligence of Things (AIoT), enhances the devices’ processing and analysis capabilities and disrupts such sectors as healthcare, industry, and oil. However, AIoT’s complexity and scale are challenging for traditional machine learning (ML). Deep learning offers a solution but has limited testability, verifiability, and interpretability. In turn, the neuro-symbolic paradigm addresses these challenges by combining the robustness of symbolic AI with the flexibility of DL, enabling AI systems to reason, make decisions, and generalize knowledge from large datasets better. This paper reviews state-of-the-art DL models for IoT, identifies their limitations, and explores how neuro-symbolic methods can overcome them. It also discusses key challenges and research opportunities in enhancing AIoT reliability with neuro-symbolic approaches, including hard-coded symbolic AI, multimodal sensor data, biased interpretability, trading-off interpretability, and performance, complexity in integrating neural networks and symbolic AI, and ethical and societal challenges. © The Author(s) 2024.

关键词： Deep learning

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A Review on the Role of Blockchain Technology for Decentralized Identity Management: A Future Without Passwords 4

A Review on the Role of Blockchain Technology for Decentrali...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Raut, Rohini Gourshettiwar, Palash Thakre, Gayatri Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Advanced identity mechanisms are essential for secure client-identifiable proof in the unambiguously computerized world. Because the board frameworks rely on aggregated databases, fraud, data breaches, and unauthorized access are significant security risks. By providing a decentralized, immutable structure, blockchain technology advances security and protection. Through Self-Sovereign Identity, blockchain gives people authority over their own data. While also improving confirmation and check mechanisms. The potential of blockchain-based Identity, this article examines the board frameworks, focusing on five essential components: authentication, privacy, trust, integrity, and simplicity. It examines how interoperability, flexibility, and collaboration with outdated frameworks prevent decentralized IDMS being more widely used. The study also covers customer satisfaction, legal concerns, and potential future developments, such as Web 3.0, security enhancements, and integration with decentralized money. By addressing these problems and looking into new possibilities, blockchain-based IDMS may be able to reform advanced identity across the entire board. © 2025 IEEE.

关键词： Decentralized finance

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Modeling Speech Emotion Recognition via ImageBind representations

Modeling Speech Emotion Recognition via ImageBind representa...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Chakhtouna, Adil Sekkate, Sara Adib, Abdellah Team Data Science & Artificial Intelligence Laboratory of Mathematics Computer Science and Applications (LMCSA) Faculty of Sciences and Technologies Hassan II University Mohammedia Morocco Higher National School of Arts and Crafts Hassan II University Casablanca Morocco

Speech Emotion Recognition (SER) refers to the ability of Machine Learning (ML) and Deep Learning (DL) techniques to accurately predict people's emotional states from speech signals. Significant progress has been achieved in the SER domain involving the incorporation of DL models to introduce novel features extraction processes. This paper introduces the use of deep representations learned from the multi-modal Large Language Model (LLM) called ImageBind. These representations were subsequently provided as input to the Nu-Support Vector Machine (Nu-SVM) with RBF kernel for the classification task. The experiments were executed using the IEMOCAP database within the context of a Speaker-Dependent (SD) scenario. The method achieved a noteworthy overall accuracy rate of 80.58% for the four emotions of IEMOCAP, representing a substantial improvement over well-established methods in the existing body of literature. Thus, affirming that the proposed methodology, founded upon ImageBind representations, introduces a novel perspective to the field of SER. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Support vector machines

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV 13

BMA for the BMA: Boosting Mobility Analysis for the Bangkok ...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kujareanpaisal, Poonnaphop Mayhasap, Rujira Tea-Makorn, Pin Pin Jindahra, Pavitra Starita, Stefano Cooharojananone, Nagul Atchariyachanvanich, Kanokwan Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Sasin School of Management Data Analytics Center Bangkok Thailand School of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (纸本)9798350373332

The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions. © 2024 IEEE.

关键词： Object detection

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RoboArm-NMP: a Learning Environment for Neural Motion Planning

arXiv

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arXiv 2024年

作者： Jurgenson, Tom Sudry, Matan Avineri, Gal Tamar, Aviv Faculty of Electrical And Computer Engineering Technion Faculty of Data and Decision Science Technion

We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulators. Our Python-based environment provides baseline implementations for learning control policies (either supervised or reinforcement learning based), a simulator based on PyBullet, data of solved instances using a classical motion planning solver, various representation learning methods for encoding the obstacles, and a clean interface between the learning and planning frameworks. Using RoboArm-NMP, we compare several prominent NMP design points, and demonstrate that the best methods mostly succeed in generalizing to unseen goals in a scene with fixed obstacles, but have difficulty in generalizing to unseen obstacle configurations, suggesting focus points for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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A Robust Framework for Epileptic Seizure Diagnosis: Utilizing GRU-CNN Architectures in EEG Signal Analysis 20

A Robust Framework for Epileptic Seizure Diagnosis: Utilizin...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Nikoupour, Maryam Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Faculty of Engineering Department of Computer Engineering Tehran Iran Iranian Research Organization for Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository's epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods. © 2024 IEEE.

关键词： Electroencephalography

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Python package for causal discovery based on LiNGAM

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Journal of Machine Learning Research 2023年第1期24卷 1-8页

作者： Ikeuchi, Takashi Ide, Mayumi Zeng, Yan Maeda, Takashi Nicholas Shimizu, Shohei SCREEN Advanced System Solutions Co. Ltd. Japan Department of Computer Science and Technology Tsinghua University China School of System Design and Technology Tokyo Denki University Japan Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Japan

Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a well-known model for causal discovery. This paper describes an open-source Python package for causal discovery based on LiNGAM. The package implements various LiNGAM methods under different settings like time series cases, multiple-group cases, mixed data cases, and hidden common cause cases, in addition to evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://***/cdt15/lingam. ©2023 Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, and Shohei Shimizu.

关键词： Python

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Critical Evaluation of the Role of Cloud Systems and Networking in the Security and Growth of the Business Market 2

Critical Evaluation of the Role of Cloud Systems and Network...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Fakhouri, Hussam N. Alhadidi, Basim Alsharaiah, Mohammad A. Al Naddaf, Heba Alomoush data, Ashraf Saleh University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman Jordan Al-Balqa Applied University Department Of Computer Information Systems Salt19117 Jordan Al-Ahliyya Amman University Data Science And Artificial Intelligence Department Amman Jordan Damascus University Faculty Of Media Deprtment Of Electronic Media Damascus Syria University Of Petra Faculty Of Information Technology Science And Artificial Intelligence Department Amman Jordan

ISBN: (纸本)9798350394962

This paper aims to assesses the multifaceted role of cloud systems in stimulating and protecting business market growth, with the goal of exploring the transformative benefits of cloud computing, such as lower costs, increased flexibility, improved data security, efficient collaboration, and greater integration, while also highlighting the challenges and risks associated with it. These include data protection, legal concerns, and compliance, which are especially critical given the growing incidence of cybersecurity risks. Furthermore, the paper dives into the many types of cloud services, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), outlining their distinct benefits, potential downsides, and applicability in various business situations. It also looks at new trends and solutions, particularly the rise of hybrid cloud systems. © 2024 IEEE.

关键词： Infrastructure as a service (IaaS)

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