Suchergebnisse - Engineering::Computer science AND engineering::Computing methodologies::Artificial intelligence

An Imperceptible Data Augmentation Based Blackbox Clean-Label Backdoor Attack on Deep Neural Networks

Autoren: Chaohui Xu Wenye Liu Yue Zheng et al.

Weitere Verfasser: Chaohui Xu Wenye Liu Yue Zheng et al.

Quelle: IEEE Transactions on Circuits and Systems I: Regular Papers. 70:5011-5024

Schlagwörter: Deep Neural Networks, 9. Industry and infrastructure, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], 02 engineering and technology, Data Augmentation, Clean-Label Backdoor Attack, Computer science and engineering::Computing methodologies::Image processing and computer vision [Engineering], 0202 electrical engineering, electronic engineering, information engineering, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision, Data Poisoning, Edge AI

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/10356/173118

FAT: An In-Memory Accelerator With Fast Addition for Ternary Weight Neural Networks

Autoren: Shien Zhu Luan H. K. Duong Hui Chen et al.

Weitere Verfasser: Shien Zhu Luan H. K. Duong Hui Chen et al.

Quelle: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42:781-794

Schlagwörter: FOS: Computer and information sciences, Computer Science - Artificial Intelligence, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Spin-Transfer Torque Magnetic Random-Access Memory, Convolutional Neural Network, 02 engineering and technology, Engineering::Computer science and engineering::Hardware::Arithmetic and logic structures, 7. Clean energy, Computer science and engineering::Hardware::Arithmetic and logic structures [Engineering], Artificial Intelligence (cs.AI), Hardware Architecture (cs.AR), Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems, 0202 electrical engineering, electronic engineering, information engineering, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Computer Science - Hardware Architecture, Computer science and engineering::Computer systems organization::Special-purpose and application-based systems [Engineering], Ternary Wight Neural Network, In-Memory Computing

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2201.07634
https://hdl.handle.net/10356/162483

Probabilistic Conflict Detection Using Heteroscedastic Gaussian Process and Bayesian Optimization

Autoren: Duc-Thinh Pham Yash Guleria Sameer Alam et al.

Weitere Verfasser: Duc-Thinh Pham Yash Guleria Sameer Alam et al.

Quelle: IEEE Access, Vol 11, Pp 109341-109352 (2023)

Schlagwörter: Uncertainty Modeling, uncertainty modeling, Bayesian Optimization, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Engineering::Aeronautical engineering::Aviation, probabilistic conflict detection, TK1-9971, Air traffic management, ADS-B data, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Electrical engineering. Electronics. Nuclear engineering, Aeronautical engineering::Aviation [Engineering], Probabilistic Conflict Detection, Bayesian optimization

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/09b9e5c44ec64d7899484edb4cddf10a
https://hdl.handle.net/10356/171177

SNIFF: Reverse Engineering of Neural Networks With Fault Attacks

Autoren: Jakub Breier Dirmanto Jap Xiaolu Hou et al.

Weitere Verfasser: Jakub Breier Dirmanto Jap Xiaolu Hou et al.

Quelle: IEEE Transactions on Reliability. 71:1527-1539

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Engineering::Computer science and engineering::Hardware::Performance and reliability, Fault Attacks, Computer science and engineering::Hardware::Performance and reliability [Engineering], 02 engineering and technology, Cryptography [Library and information science], Machine Learning (cs.LG), Deep Learning, 0202 electrical engineering, electronic engineering, information engineering, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Library and information science::Cryptography, Cryptography and Security (cs.CR)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/2002.11021
http://arxiv.org/abs/2002.11021
https://ieeexplore.ieee.org/abstract/document/9530205/
https://silicon-austria-labs.elsevierpure.com/en/publications/sniff-reverse-engineering-of-neural-networks-with-fault-attacks
https://dblp.uni-trier.de/db/journals/corr/corr2002.html#abs-2002-11021
https://hdl.handle.net/10356/155678

Ownership Verification of DNN Architectures via Hardware Cache Side Channels

Autoren: Xiaoxuan Lou Shangwei Guo Jiwei Li et al.

Weitere Verfasser: Xiaoxuan Lou Shangwei Guo Jiwei Li et al.

Quelle: IEEE Transactions on Circuits and Systems for Video Technology. 32:8078-8093

Schlagwörter: FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Deep Neural Network, 02 engineering and technology, Watermarking, Engineering::Computer science and engineering, Cache Side Channel, 0202 electrical engineering, electronic engineering, information engineering, Computer science and engineering [Engineering], Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Cryptography and Security (cs.CR)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2102.03523
https://hdl.handle.net/10356/159773

Easy-But-Effective Domain Sub-Similarity Learning for Transfer Regression

Autoren: Pengfei Wei Ramon Sagarna Yiping Ke et al.

Weitere Verfasser: Pengfei Wei Ramon Sagarna Yiping Ke et al.

Quelle: IEEE Transactions on Knowledge and Data Engineering. 34:4161-4171

Schlagwörter: Transfer Covariance Function, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], 0202 electrical engineering, electronic engineering, information engineering, Transfer Regression, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, 02 engineering and technology, Gaussian Process

Dateibeschreibung: application/pdf

Zugangs-URL: https://dr.ntu.edu.sg/bitstream/10356/147569/2/Easy-but-effective%20Domain%20Sub-similarity%20Learning%20for%20Transfer%20Regression-TKDE2020.pdf
https://ieeexplore.ieee.org/document/9266749
https://dr.ntu.edu.sg/bitstream/10356/147569/2/Easy-but-effective%20Domain%20Sub-similarity%20Learning%20for%20Transfer%20Regression-TKDE2020.pdf
https://dr.ntu.edu.sg/handle/10356/147569
https://hdl.handle.net/10356/147569

A Generative Adversarial Imitation Learning Approach for Realistic Aircraft Taxi-Speed Modeling

Autoren: Duc-Thinh Pham Thanh-Nam Tran Sameer Alam et al.

Weitere Verfasser: Duc-Thinh Pham Thanh-Nam Tran Sameer Alam et al.

Quelle: IEEE Transactions on Intelligent Transportation Systems. 23:2509-2522

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling, Engineering::Aeronautical engineering::Flight simulation, Computer science and engineering::Computing methodologies::Artificial intelligence [Engineering], Aeronautical engineering::Flight simulation [Engineering], 0202 electrical engineering, electronic engineering, information engineering, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Generative Adversarial Imitation Learning, 02 engineering and technology, Computer science and engineering::Computing methodologies::Simulation and modeling [Engineering], Aircraft Ground Movement

Dateibeschreibung: application/pdf

Zugangs-URL: https://dr.ntu.edu.sg/bitstream/10356/152909/2/IEEE_T_ITS_FINAL_VERSION.pdf
https://ieeexplore.ieee.org/abstract/document/9576546/metrics
https://hdl.handle.net/10356/152909

A Mouse-Trajectory Based Model for Predicting Query-URL Relevance

Autoren: Song, Hengjie Liao, Ruoxue Zhang, Xiangliang et al.

Weitere Verfasser: Song, Hengjie Liao, Ruoxue Zhang, Xiangliang et al.

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence. 26:143-149

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence [DRNTU], 02 engineering and technology

Zugangs-URL: http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/4968
https://hdl.handle.net/10356/104498
http://hdl.handle.net/10220/18850

Unbiased multiple instance learning for weakly supervised video anomaly detection

Autoren: Lv, Hui Yue, Zhongqi Sun, Qianru et al.

Weitere Verfasser: Lv, Hui Yue, Zhongqi Sun, Qianru et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Multiple Instance Learning, Weakly Supervised Video Anomaly Detection

Dateibeschreibung: application/pdf

Relation: A20E6c0101; AISG2-RP-2021-022; Lv, H., Yue, Z., Sun, Q., Luo, B., Cui, Z. & Zhang, H. (2023). Unbiased multiple instance learning for weakly supervised video anomaly detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). https://dx.doi.org/10.1109/CVPR52729.2023.00775; https://hdl.handle.net/10356/169509; https://cvpr2023.thecvf.com/Conferences/2023

Verfügbarkeit: https://hdl.handle.net/10356/169509
https://doi.org/10.1109/CVPR52729.2023.00775
https://cvpr2023.thecvf.com/Conferences/2023

CPMR: context-aware incremental sequential recommendation with pseudo-multi-task learning

Autoren: Bian, Qingtian Xu, Jiaxing Fang, Hui et al.

Weitere Verfasser: Bian, Qingtian Xu, Jiaxing Fang, Hui et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Recommender Systems, Incremental Recommendation, Context-Aware Recommendation, Graph Neural Networks, Pseudo-Multi-Task Learning

Dateibeschreibung: application/pdf

Relation: MOE-T2EP20220-0006; IAF-PP; https://hdl.handle.net/10356/170988; October 2023; 120; 130

Verfügbarkeit: https://hdl.handle.net/10356/170988
https://doi.org/10.1145/3583780.3615512

HypLiLoc: towards effective LiDAR pose regression with hyperbolic fusion

Autoren: Wang, Sijie Kang, Qiyu She, Rui et al.

Weitere Verfasser: Wang, Sijie Kang, Qiyu She, Rui et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Visual Relocalization, Sensors

Dateibeschreibung: application/pdf

Relation: A19D6a0053; Wang, S., Kang, Q., She, R., Wang, W., Zhao, K., Song, Y. & Tay, W. P. (2023). HypLiLoc: towards effective LiDAR pose regression with hyperbolic fusion. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023). https://dx.doi.org/10.1109/CVPR52729.2023.00501; https://hdl.handle.net/10356/165273; https://cvpr2023.thecvf.com/Conferences/2023

Verfügbarkeit: https://hdl.handle.net/10356/165273
https://doi.org/10.1109/CVPR52729.2023.00501
https://cvpr2023.thecvf.com/Conferences/2023

Privacy-enhanced knowledge transfer with collaborative split learning over teacher ensembles

Autoren: Liu, Ziyao Guo, Jiale Yang, Mengmeng et al.

Weitere Verfasser: Liu, Ziyao Guo, Jiale Yang, Mengmeng et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Privacy-Preservation, Knowledge Transfer

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/10356/172524

Verfügbarkeit: https://hdl.handle.net/10356/172524
https://doi.org/10.1145/3591197.3591303

CAP : Context-aware Pruning for semantic segmentation

Autoren: He, Wei Wu, Meiqing Liang, Mingfu et al.

Weitere Verfasser: He, Wei Wu, Meiqing Liang, Mingfu et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision, Network Compression, Semantic Segmentation

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/10356/147439; 960; 969

Verfügbarkeit: https://hdl.handle.net/10356/147439

Toward better grade prediction via A2GP - an academic achievement inspired predictive model

Autoren: Qiu, Wei Supraja, S. Khong, Andy Wai Hoong et al.

Weitere Verfasser: Qiu, Wei Supraja, S. Khong, Andy Wai Hoong et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Grade Prediction, Machine Learning, Attention Mechanism, Long Short-Term Memory Network, Graph Convolutional Network

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/10356/165580; https://educationaldatamining.org/edm2022/proceedings/; 195; 205

Verfügbarkeit: https://hdl.handle.net/10356/165580
https://doi.org/10.5281/ZENODO.6852984
https://educationaldatamining.org/edm2022/proceedings/

A randomized link transformer for diverse open-domain dialogue generation

Autoren: Lee, Jing Yang Lee, Kong Aik Gan, Woon-Seng et al.

Weitere Verfasser: Lee, Jing Yang Lee, Kong Aik Gan, Woon-Seng et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Randomized Link Transformer, Open-Domain Dialogue Generation

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/10356/159793; 11

Verfügbarkeit: https://hdl.handle.net/10356/159793
https://doi.org/10.18653/v1/2022.nlp4convai-1.1
https://aclanthology.org/volumes/2022.nlp4convai-1/

Generating personalized dialogue via multi-task meta-learning

Autoren: Lee, Jing Yang Lee, Kong Aik Gan, Woon-Seng et al.

Weitere Verfasser: Lee, Jing Yang Lee, Kong Aik Gan, Woon-Seng et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Multi-Task Meta-Learning, Personalized Dialogue Generation

Dateibeschreibung: application/pdf

Relation: https://hdl.handle.net/10356/153442; 88; 97

Verfügbarkeit: https://hdl.handle.net/10356/153442
https://semdial2021.ling.uni-potsdam.de/assets/semdial2021_potsdial_full_proceedings.pdf

A FeedForward–Convolutional Neural Network to detect low-rate DoS in IoT

Autoren: Ilango, Harun Surej Ma, Maode Su, Rong et al.

Weitere Verfasser: Ilango, Harun Surej Ma, Maode Su, Rong et al.

Schlagwörter: Engineering, Engineering::Electrical and electronic engineering, Engineering::Electrical and electronic engineering::Wireless communication systems, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Internet of Things, Software-Defined Networking, Deep Learning, Low-Rate DoS Attacks, CIC DoS 2017, Anomaly Detection, Network Security

Dateibeschreibung: application/pdf

Relation: A19D6a0053; Engineering Applications of Artificial Intelligence; https://hdl.handle.net/10356/167102; 114; 105059

Verfügbarkeit: https://hdl.handle.net/10356/167102
https://doi.org/10.1016/j.engappai.2022.105059

A machine learning perspective on fNIRS signal quality control approaches

Autoren: Bizzego, Andrea Neoh, Michelle Gabrieli, Giulio et al.

Weitere Verfasser: Bizzego, Andrea Neoh, Michelle Gabrieli, Giulio et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Social sciences::Psychology, Deep Learning, fNIRS, Machine Learning, Signal Quality Control

Dateibeschreibung: application/pdf

Relation: IEEE Transactions on Neural Systems and Rehabilitation Engineering; https://hdl.handle.net/10356/163047; 30; 2292; 2300

Verfügbarkeit: https://hdl.handle.net/10356/163047
https://doi.org/10.1109/TNSRE.2022.3198110

Aircraft trajectory prediction with enriched intent using encoder-decoder architecture

Autoren: Tran, Phu N. Nguyen, Hoang Quang Viet Pham, Duc-Thinh et al.

Weitere Verfasser: Tran, Phu N. Nguyen, Hoang Quang Viet Pham, Duc-Thinh et al.

Schlagwörter: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Aircraft Trajectory Prediction, Encoder-Decode, Recurrent Neural Network

Dateibeschreibung: application/pdf

Relation: IEEE Access; https://hdl.handle.net/10356/155806; 10; 17881; 17896

Verfügbarkeit: https://hdl.handle.net/10356/155806
https://doi.org/10.1109/ACCESS.2022.3149231

Mind the portability : a warriors guide through realistic profiled side-channel analysis

Autoren: Bhasin, Shivam Chattopadhyay, Anupam Heuser, Annelie et al.

Weitere Verfasser: Bhasin, Shivam Chattopadhyay, Anupam Heuser, Annelie et al.

Schlagwörter: Science::Mathematics::Discrete mathematics::Cryptography, Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence, Profiled Side-channel Attacks, Digital Devices

Dateibeschreibung: application/pdf

Relation: NRF2018–NCR–NCR009–0001; https://hdl.handle.net/10356/148354; 15

Verfügbarkeit: https://hdl.handle.net/10356/148354
https://doi.org/10.14722/ndss.2020.24390