检索结果-内蒙古大学图书馆

Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

学校读者我要写书评

暂无评论

Computational intelligence and neuroscience 2023年第1期2023卷 4776770页

作者： Sujatha Krishnamoorthy Yaxi Zhang Seifedine Kadry Muhammad Attique Khan Majed Alhaisoni Nasser Mustafa Weifeng Yu Abdullah Alqahtani Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Wenzhou Zhejiang Province China. Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Wenzhou Zhejiang Province China. Department of Neurology Wenzhou Central Hospital Medical Group Wenzhou 325000 China. Department of Applied Data Science Noroff University College Kristiansand Norway. Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 UAE. Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Computer Science HITEC University Taxila Pakistan. Computer Sciences Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh 11671 Saudi Arabia. Wenzhou-Kean University School of Science and Technology Wenzhou Zhejiang Province China. College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia.

Malfunctions in the immune system cause multiple sclerosis (MS), which initiates mild to severe nerve damage. MS will disturb the signal communication between the brain and other body parts, and early diagnosis will help reduce the harshness of MS in humankind. Magnetic resonance imaging (MRI) supported MS detection is a standard clinical procedure in which the bio-image recorded with a chosen modality is considered to assess the severity of the disease. The proposed research aims to implement a convolutional neural network (CNN) supported scheme to detect MS lesions in the chosen brain MRI slices. The stages of this framework include (i) image collection and resizing, (ii) deep feature mining, (iii) hand-crafted feature mining, (iii) feature optimization with firefly algorithm, and (iv) serial feature integration and classification. In this work, five-fold cross-validation is executed, and the final result is considered for the assessment. The brain MRI slices with/without the skull section are examined separately, presenting the attained results. The experimental outcome of this study confirms that the VGG16 with random forest (RF) classifier offered a classification accuracy of >98% MRI with skull, and VGG16 with K-nearest neighbor (KNN) provided an accuracy of >98% without the skull.

关键词：

SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Najafipour, Saeed Hosseini, Saeid Hua, Wen Kangavari, Mohammad Reza Zhou, Xiaofang Computational Cognitive Model Research Lab School of Computer Engineering Iran University of Science and Technology Iran Cyber Security Lab. Singapore University of Technology and Design Singapore School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional text mining methods fail to effectively extract concepts through words and phrases. Thirdly, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using the complementary knowledge-bases makes the results biased to the content of the external database and deviates the understanding and interpretation away from the real nature of the given short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the tightly connected author subgraphs from microblog short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. Additionally, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be. Copyright © 2019, The Authors. All rights reserved.

关键词： Vector spaces

Integrating privacy enhancing techniques into blockchains using sidechains

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Parizi, Reza M. Homayoun, Sajad Yazdinejad, Abbas Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Software Engineering and Game Development Kennesaw State University GA United States Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran Faculty of Computer Engineering University of Isfahan School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas San AntonioTX United States

Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and serve as the foundation for Internet's security infrastructure. The immutable nature of the blockchain makes it a winner on security and transparency;it is nearly inconceivable for ledgers to be altered in a way not instantly clear to every single user involved. However, most blockchains fall short in privacy aspects, particularly in data protection. Garlic Routing and Onion Routing are two of major Privacy Enhancing Techniques (PETs) which are popular for anonymization and security. Garlic Routing is a methodology using by I2P Anonymous Network to hide the identity of sender and receiver of data packets by bundling multiple messages into a layered encryption structure. The Onion Routing attempts to provide lowlatency Internet-based connections that resist traffic analysis, deanonymization attack, eavesdropping, and other attacks both by outsiders (e.g. Internet routers) and insiders (Onion Routing servers themselves). As there are a few controversies over the rate of resistance of these two techniques to privacy attacks, we propose a PET-Enabled Sidechain (PETES) as a new privacy enhancing technique by integrating Garlic Routing and Onion Routing into a Garlic Onion Routing (GOR) framework suitable to the structure of blockchains. The preliminary proposed GOR aims to improve the privacy of transactions in blockchains via PETES structure. Copyright © 2019, The Authors. All rights reserved.

关键词： Blockchain

Efficient Large-Scale Multi-graph Similarity Search Using MapReduce 16th

学校读者我要写书评

暂无评论

Efficient Large-Scale Multi-graph Similarity Search Using Ma...

16th Web information Systems and Applications Conference, WISA 2019

作者： Pang, Jun Yu, Minghe Gu, Yu School of Computer Science and Technology Wuhan University of Science and Technology WuhanHubei430065 China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen University Shenzhen518061 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System WuhanHubei430065 China School of Computer Science and Engineering Northeastern University ShenyangLiaoning110819 China College of Software Northeastern University ShenyangLiaoning110819 China

ISBN: (纸本)9783030309510

A multi-graph is a set consisting of multiple graphs. Multi-graph similarity search aims to find the multi-graphs similar to the query multi-graphs from the multi-graph datasets. It plays important role in a wide range of application fields, such as finding similar drugs, searching similar molecule groups and so on. However, existing algorithms of multi-graph similarity search are memory-based algorithms, which are not suitable for the large amount of multi-graph scenarios. In this paper, we propose a parallel algorithm based on the MapReduce programming model to solve the problem of the large-scale multi-graph similarity search. Our proposed algorithm consists of two MapReduce jobs, one for indexing and the other for filtering and validation. Specially, we adapt the localization strategy to further improve the performance of our algorithm, which not only reduces the communication cost, but also mitigates the load imbalance. Extensive experimental results show that our algorithm is effective and efficient. © 2019, Springer Nature Switzerland AG.

关键词： MapReduce

Cross-ethnicity face anti-spoofing recognition challenge: A review

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Ajian Li, Xuan Wan, Jun Liang, Yanyan Escalera, Sergio Escalante, Hugo Jair Madadi, Meysam Jin, Yi Wu, Zhuoyuan Yu, Xiaogang Tan, Zichang Yuan, Qi Yang, Ruikun Zhou, Benjia Guo, Guodong Li, Stan Z. Faculty of Information Technology Avenida WaiLong Taipa Macau China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Universitat de Barcelona and Computer Vision Center Barcelona Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Mexico School of Software Beihang University Beijing China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Beijing Westlake University Hangzhou China

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing. Recently, a multi-ethnic face anti-spoofing dataset, CASIA-SURF CeFA, has been released with the goal of measuring the ethnic bias. It is the largest up to date cross-ethnicity face anti-spoofing dataset covering 3 ethnicities, 3 modalities, 1;607 subjects, 2D plus 3D attack types, and the first dataset including explicit ethnic labels among the recently released datasets for face *** organized the Chalearn Face Anti-spoofing Attack Detection Challenge which consists of single-modal (e.g., RGB) and multi-modal (e.g., RGB, Depth, Infrared (IR)) tracks around this novel resource to boost research aiming to alleviate the ethnic bias. Both tracks have attracted 340 teams in the development stage, and finally 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Large dataset

Automatic and Robust Skull Registration Based on Discrete Uniformization

学校读者我要写书评

暂无评论

Automatic and Robust Skull Registration Based on Discrete Un...

International Conference on computer Vision (ICCV)

作者： Junli Zhao Xin Qi Chengfeng Wen Na Lei Xianfeng Gu School of Data Science and Software Engineering Qingdao University Qingdao China Department of Computer Science Stony Brook University Stony Brook USA International School of Information Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Skull registration plays a fundamental role in forensic science and is crucial for craniofacial reconstruction. The complicated topology, lack of anatomical features, and low quality reconstructed mesh make skull registration challenging. In this work, we propose an automatic skull registration method based on the discrete uniformization theory, which can handle complicated topologies and is robust to low quality meshes. We apply dynamic Yamabe flow to realize discrete uniformization, which modifies the mesh combinatorial structure during the flow and conformally maps the multiply connected skull surface onto a planar disk with circular holes. The 3D surfaces can be registered by matching their planar images using harmonic maps. This method is rigorous with theoretic guarantee, automatic without user intervention, and robust to low mesh quality. Our experimental results demonstrate the efficiency and efficacy of the method.

关键词： Three-dimensional displays Measurement Topology Robustness Iterative closest point algorithm Surface morphology Face

MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using Meta-Learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

Fical: Focal inter-class angular loss for image classification 34

学校读者我要写书评

暂无评论

Fical: Focal inter-class angular loss for image classificati...

34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019

作者： Wei, Xinran Chang, Dongliang Xie, Jiyang Zheng, Yixiao Gong, Chen Zhang, Chuang Ma, Zhanyu Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab Beijing China Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education PCA Lab China Jiangsu Key Lab of Image and Video Understanding for Social Security China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728137230

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets. © 2019 IEEE.

关键词： Image classification