检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Mentxaka, Oier Díaz-Rodríguez, Natalia Coeckelbergh, Mark de Prado, Marcos López Gómez, Emilia Llorca, David Fernández Herrera-Viedma, Enrique Herrera, Francisco Dept. of Computer Science and Artificial Intelligence DaSCI University of Granada Spain Dept. of Philosophy University of Vienna Vienna Austria School of Engineering Cornell University IthacaNY United States Dept. of Mathematics Khalifa University of Science and Technology Abu Dhabi United Arab Emirates ADIA Lab Al Maryah Island Abu Dhabi United Arab Emirates Joint Research Centre European Commission Seville Spain Computer Engineering Dept. University of Alcalá Alcalá de Henares Spain

Artificial Intelligence (AI) poses both significant risks and valuable opportunities for democratic governance. This paper introduces a dual taxonomy to evaluate AI’s complex relationship with democracy: the AI Risks to Democracy (AIRD) taxonomy, which identifies how AI can undermine core democratic principles such as autonomy, fairness, and trust;and the AI’s Positive Contributions to Democracy (AIPD) taxonomy, which highlights AI’s potential to enhance transparency, participation, efficiency, and evidence-based policymaking. Grounded in the European Union’s approach to ethical AI governance, and particularly the seven Trustworthy AI requirements proposed by the European Commission’s High-Level Expert Group on AI, each identified risk is aligned with mitigation strategies based on EU regulatory and normative frameworks. Our analysis underscores the transversal importance of transparency and societal wellbeing across all risk categories and offers a structured lens for aligning AI systems with democratic values. By integrating democratic theory with practical governance tools, this paper offers a normative and actionable framework to guide research, regulation, and institutional design to support trustworthy, democratic AI. It provides scholars with a conceptual foundation to evaluate the democratic implications of AI, equips policymakers with structured criteria for ethical oversight, and helps technologists align system design with democratic principles. In doing so, it bridges the gap between ethical aspirations and operational realities, laying the groundwork for more inclusive, accountable, and resilient democratic systems in the algorithmic age. © 2025, CC BY.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

A Machine Vision-Based Person Detection Under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System

A Machine Vision-Based Person Detection Under Low-Illuminanc...

引用

Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Julie Ann B. Susa Sammy V. Militante Aimee G. Acoba Ryan Soriente Evangelista Luisito Lolong Lacatan Charlemagne G. Laviňa Bartolome T. Tanguilig Dept. of Computer Engineering Southern Luzon State University Lucban Quezon Philippines College of Engineering and Architecture University of Antique Antique Philippines Electronics Eng'g Technology Department Technological University of the Philippines Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan ng Cabuyao Laguna Philippines

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance.

关键词： Deep learning Training Visualization computer vision Heuristic algorithms Computational modeling Lighting

来源：评论

学校读者我要写书评

暂无评论

Auditory models comparison for horizontal localization of concurrent speakers in adverse acoustic scenarios 23

Auditory models comparison for horizontal localization of co...

引用

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Barumerli, Roberto Almenari, Andrea Geronazzo, Michele Di Nunzio, Giorgio Maria Avanzini, Federico Dept. of Information Engineering University of Padova Italy Dept. of Architecture Design and Media Technology Aalborg University Denmark Dept. of Computer Science University of Milano Italy

ISBN: (纸本)9783939296157

This paper aims at comparing and reproducing the predictions of two public available computational auditory models for speaker localization in different simulated environments. The direction-of-arrival (DOA) of sound sources in the horizontal plane can be extracted by using binaural spatial cues from room and user acoustics. Since our predictions consider the specificity of both models at the level of peripheral processing, the proposed solution for DOA extraction also provides a common multi-conditional training for the Gaussian Mixture Model (GMM) approach. A set of acoustic simulations of adverse conditions (i.e. multi speakers or high reverberant scenarios) supports the evaluation phase on robustness of the synthetic auditory process. Our analysis reproduces two case studies from the scientific literature in order to investigate the reliability of localization predictions in the frontal horizontal plane. Finally, a newly defined acoustic scenario allows to identify differences between auditory models outcome in the entire horizontal plane. The results show a good agreement with previous literature and our machine learning approach emphasizes peculiarities of each approach for auditory peripheral processing. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Auditory model Binaural processing Room acoustic simulation

来源：评论

学校读者我要写书评

暂无评论

On the evaluation of head-related transfer functions with probabilistic auditory models of human sound localization 23

On the evaluation of head-related transfer functions with pr...

引用

23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Geronazzo, Michele Barumerli, Roberto Avanzini, Federico Dept. of Architecture Design and Media Technology Aalborg University Denmark Dept. of Information Engineering University of Padova Italy Dept. of Computer Science University of Milano Italy

ISBN: (纸本)9783939296157

Understanding spatial hearing leads to implement efficient and effective auralization rendering algorithms with headphones. Two important aspects contribute to sound localization: (i) acoustic filtering of listener body, and (ii) non-acoustic factors introduced by auditory periphery. Accordingly, head-related transfer functions (HRTFs) describe users acoustics in terms of their spatial filtering. Binaural synthesis through generic HRTFs (commonly a dummy head) is the most simple solution for an auralization framework. In this scenario, a high variability in localization tasks between subjects yields to an unreliable rendering. Listener's acoustic and perceptual characterization require HRTF modeling and auditory models predictions in order to provide an effective auralization on individual basis. Systemic comparisons of HRTF approximations and different user profiles can help to predict listener's performances. We consider a case study on both vertical and horizontal localization with different HRTFs and two probabilistic auditory models. In our analysis, spatial audio rendering with non-individual HRTFs has a special attention for its commercial relevance compared to unpractical and questionable use of individual HRTFs. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Auditory model Head-related transfer function Localization Non-individual HRTF

来源：评论

学校读者我要写书评

暂无评论

Reliability assurance in early-life-failure test through improved nearest neighbor regression

引用

IEICE Electronics Express 2020年第2期17卷

作者： Song, Tai Liang, Huaguo Huang, Zhengfeng Hou, Jie Dept. of Electronic Science and Applied Physics Hefei University of Technology Hefei230009 China Institute of Computer Architecture and Computer Stuttgart University StuttgartD-70569 Germany

During manufacturing test, researchers usually overlook the importance of process variation defects and marginal defects, which can seriously affect test results of Early-Life-Failure (ELF). Theoretically, machine learning classification methods can be used to identify these two defects. In fact, when features overfitting or data distribution overlap seriously, classifiers perform poorly, it will not achieve the desired results. This paper first-ever proposes a kind of data preprocessing method combines improved K-Nearest Neighbors (KNN) regression classifier, so that the classification results will be enhanced in terms of classification performance. Experiment results demonstrate that the predictive accuracy is 45% higher than the traditional logistic regression method. This proposed method will drive critical new requirements for fault modeling, test generation and test application, and implementing them effectively will require a new level of collaboration between process and product developers. © 2020 Institute of Electronics Information Communication Engineers. All rights reserved.

关键词： Defects

来源：评论

学校读者我要写书评

暂无评论

Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv

引用

arXiv 2022年

作者： Moridian, Parisa Ghassemi, Navid Jafari, Mahboobeh Salloum-Asfar, Salam Sadeghi, Delaram Khodatars, Marjane Shoeibi, Afshin Khosravi, Abbas Ling, Sai Ho Subasi, Abdulhamit Alizadehsani, Roohallah Gorriz, Juan M. Abdulla, Sara A. Acharya, U. Rajendra Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Deakin University GeelongVIC Australia UltimoNSW Australia Institute of Biomedicine Faculty of Medicine University of Turku Turku Finland Dept. of Computer Science College of Engineering Effat University Jeddah Saudi Arabia Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming;therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

arXiv

引用

arXiv 2022年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Khosravi, Abbas Zare, Assef Gorriz, Juan M. Chale-Chale, Amir Hossein Khadem, Ali Rajendra Acharya, U. Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University Geelong Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The university of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy. © 2022, CC BY-NC-ND.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Dynamic Resilient Graph Games for State-Dependent Jamming Attacks Analysis on Multi-Agent Systems

引用

IFAC-PapersOnLine 2020年第2期53卷 3421-3426页

作者： Yurid Nugraha Ahmet Cetinkaya Tomohisa Hayakawa Hideaki Ishii Quanyan Zhu Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan Dept. of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Dept. of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

A cybersecurity problem for a multi-agent consensus problem is investigated through a dynamic game formulation. Specifically, we consider a game repeatedly played between a jamming attacker and a defender. The attacker attempts to jam the links between a number of agents to delay their consensus. On the other hand, the defender tries to maintain the connection between agents by attempting to recover some of the jammed links with the goal of achieving faster consensus. In each game, the players decide which links to attack/recover and for how long to continue doing so based on a Lyapunov-like function representing the largest difference between the states of the agents. We analyze the subgame perfect equilibrium of the game and obtain an upper bound of the consensus time that is influenced by the strategies of the players. The results are illustrated with a numerical example.

关键词： Multi-agent systems Consensus problem Game theory Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

The new generation of the Data Acquisition System of KM3NeT 38

The new generation of the Data Acquisition System of KM3NeT

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Circella, M. Battista, N. Benfenati, F. Bruno, R. Buis, E.-J. Calvo, D. Chiarusi, T. Cocimano, R. Colonges, S. D’Amico, A. Díaz, A.F. Filippini, F. Henry, S. Jansweijer, P. Lindsey Clark, M. Mongelli, M. Nicolau, C. Pastore, C. Pellegrini, G. Pulvirenti, S. Real, D. Samteleben, D. Schmelling, J.-W. Sgura, I. Tatone, F. Valieri, C. Van Beveren, V. Vivolo, D. Istituto Nazionale di Fisica Nucleare – Sezione di Bari Via Orabona 4 Bari70125 Italy Istituto Nazionale di Fisica Nucleare – Sezione di Bologna V.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Dipartimento di Fisica e Astronomia V. le C. Berti-Pichat 6/2 Bologna40127 Italy Istituto Nazionale di Fisica Nucleare – Sezione di Catania Via Santa Sofia 64 Catania95123 Italy TNO Technical Sciences PO Box 155 Delft2600 AD Netherlands Nikhef National Institute for Subatomic Physics PO Box 41882 Amsterdam1009 DB Netherlands c/Catedrático José Beltrán 2 Paterna Valencia46980 Spain Istituto Nazionale di Fisica Nucleare Laboratori Nazionali del Sud Via S. Sofia 62 Catania95123 Italy Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France University of Granada Dept. of Computer Architecture and Technology/CITIC Granada18071 Spain Aix Marseille Univ. CNRS IN2P3 CPPM Marseille France Istituto Nazionale di Fisica Nucleare – Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Istituto Nazionale di Fisica Nucleare – Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università degli Studi della Campania "Luigi Vanvitelli" Dipartimento di Matematica e Fisica V.le Lincoln 5 Caserta81100 Italy

The KM3NeT Collaboration is installing two neutrino telescopes at the bottom of the Mediterranean Sea, ARCA and ORCA. These detectors are built in the form of very large 3D arrays of photomultipliers (PMTs) detecting the Cherenkov light induced by relativist particles propagating in the sea water. The PMTs are installed inside pressure-resistant glass spheres, the Digital Optical Modules (DOMs), each containing 31 PMTs together with the electronics for data taking and communication. Eighteen DOMs and a base module are comprised in a detection unit (DU), a structure standing on the sea bottom with a height of almost 700 m for ARCA (200 m for ORCA). For optimal performance of the detectors, a good synchronization of all parts of the detectors is required. The data acquisition system of KM3NeT has been designed based on the White Rabbit (WR) protocol. In a first phase of construction of the detectors, this has been developed in a tailored design based on a "broadcast" approach: in such scheme, the communications from the shore station to the detectors is performed by distributing the same data stream to all underwater nodes, while in the opposite path each single DOM sends data to shore through its own communication channel. For the next phase of construction, a new system has been developed, based on the standard WR protocol. This approach has some significant advantages, in terms of maintainability and scalability of the system, which are obtained with a non-negligible cost, however, since it became necessary to design tailored WR components which would meet the KM3NeT requirements and to modify the full design of the apparatus consequently. In this paper we will present and compare these two DAQ system designs. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic ray detectors

来源：评论

学校读者我要写书评

暂无评论

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

arXiv

引用

arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：