Fetching the paper…
Reading the bibliography…
The area of Machine Learning as a Service (MLaaS) is experiencing increased implementation due to recent advancements in the AI (Artificial Intelligence) industry.
F. Bonahon, “Bouts des variétés hyperboliques de dimension 3,” Annals of Mathematics , vol. 124, no. 1, pp. 71–158, 1986
1986
Earlier work this paper cites.
P. Salmela, M. Lehtokangas, and J. Saarinen, “Neural network based digit recognition system for voice dialling in noisy environments,” Information Sciences , vol. 121, no. 3-4, pp. 171–199, 1999
1999
Earlier work this paper cites.
B. Kotelly, The art and business of speech recognition: creating the noble voice . Addison-Wesley Professional, 2003
2003
Earlier work this paper cites.
V. Jain and L. K. Saul, “Exploratory analysis and visualization of speech and music by locally linear embedding,” in 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing , vol. 3. IEEE, 2004, pp. iii–984
2004
Earlier work this paper cites.
D. Shmilovitz, “On the definition of total harmonic distortion and its effect on measurement interpretation,” IEEE Transactions on Power delivery , vol. 20, no. 1, pp. 526–528, 2005
2005
Earlier work this paper cites.
C. Plapous, C. Marro, and P. Scalart, “Improved signal-to-noise ratio estimation for speech enhancement,” IEEE transactions on audio, speech, and language processing , vol. 14, no. 6, pp. 2098–2108, 2006
2006
Earlier work this paper cites.
Y.-J. Chen, G.-J. Horng, and G.-J. Jong, “The separated speech signals combined the hybrid adaptive algorithms by using power spectral density and total harmonic distortion,” in 2007 International Conference on Multimedia and Ubiquitous Engineering (MUE’07) . IEEE, 2007, pp. 825–830
2007
Earlier work this paper cites.
A. Bahrammirzaee, “A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems,” Neural Computing and Applications , vol. 19, no. 8, pp. 1165–1195, 2010
2010
Earlier work this paper cites.
L. Xue and T. Qian, “Speech analysis based on locally linear embedding (lle),” in 2010 Sixth International Conference on Natural Computation , vol. 4. IEEE, 2010, pp. 2159–2162
2010
Earlier work this paper cites.
S. Zhang, L. Li, and Z. Zhao, “Speech emotion recognition based on supervised locally linear embedding,” in 2010 International Conference on Communications, Circuits and Systems (ICCCAS) . IEEE, 2010, pp. 401–404
2010
Earlier work this paper cites.
C. W. Lin and S. C. Luo, “Estimating total-harmonic-distortion of analog signal in time-domain,” in 2012 IEEE 18th International Mixed-Signal, Sensors, and Systems Test Workshop . IEEE, 2012, pp. 97–100
2012
Earlier work this paper cites.
H. Zhao and Y. Xiao, “A novel robust mfcc extraction method using sample-isomap for speech recognition,” International Journal of Digital Content Technology and its Applications , vol. 6, no. 19, p. 393, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
P. H. Zhang, “Study speech recognition system based on manifold learning,” Applied Mechanics and Materials , vol. 380, pp. 3762–3765, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security , 2015, pp. 1322–1333
2015
Earlier work this paper cites.
A. Stan, C. Valentini-Botinhao, M. Giurgiu, and S. King, “Phonetic segmentation of speech using step and t-sne,” in 2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD) . IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE symposium on security and privacy (SP) . IEEE, 2017, pp. 3–18
2017
Earlier work this paper cites.
C. Wu and B. Wang, “Extracting topics based on word2vec and improved jaccard similarity coefficient,” in 2017 IEEE second international conference on data science in Cyberspace (DSC) . IEEE, 2017, pp. 389–397
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Sunu and A. G. Percus, “Dimensionality reduction for acoustic vehicle classification with spectral embedding,” in 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) . IEEE, 2018, pp. 1–5
2018
Earlier work this paper cites.
X.-l. Zheng, M.-y. Zhu, Q.-b. Li, C.-c. Chen, and Y.-c. Tan, “Finbrain: when finance meets ai 2.0,” Frontiers of Information Technology & Electronic Engineering , vol. 20, no. 7, pp. 914–924, 2019
2019
Earlier work this paper cites.
M. Stefanel and U. Goyal, “Artificial intelligence & financial services: Cutting through the noise,” APIS partners, London, England, Tech. Rep. , 2019
2019
Earlier work this paper cites.
C.-Y. Low, J. Park, and A. B.-J. Teoh, “Stacking-based deep neural network: deep analytic network for pattern classification,” IEEE Transactions on Cybernetics , vol. 50, no. 12, pp. 5021–5034, 2019
2019
Earlier work this paper cites.
M. Lecuyer, V. Atlidakis, R. Geambasu, D. Hsu, and S. Jana, “Certified robustness to adversarial examples with differential privacy,” in 2019 IEEE symposium on security and privacy (SP) . IEEE, 2019, pp. 656–672
2019
Earlier work this paper cites.
P. Dasgupta and J. Collins, “A survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks,” AI Magazine , vol. 40, no. 2, pp. 31–43, 2019
2019
Earlier work this paper cites.
D. Yin, R. Kannan, and P. Bartlett, “Rademacher complexity for adversarially robust generalization,” in International conference on machine learning . PMLR, 2019, pp. 7085–7094
2019
Earlier work this paper cites.
K. Kiani and A. Baniasadi, “Speaker recognition system based on identity vector using t-sne visualization and mean-shift algorithm,” in 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS) . IEEE, 2019, pp. 1–4
2019
Earlier work this paper cites.
A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, et al. , “Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,” Information fusion , vol. 58, pp. 82–115, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
S. Zhao, X. Ma, X. Zheng, J. Bailey, J. Chen, and Y.-G. Jiang, “Clean-label backdoor attacks on video recognition models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 14 443–14 452
2020
Earlier work this paper cites.
R. A. Solovyev, M. Vakhrushev, A. Radionov, I. I. Romanova, A. A. Amerikanov, V. Aliev, and A. A. Shvets, “Deep learning approaches for understanding simple speech commands,” in 2020 IEEE 40th international conference on electronics and nanotechnology (ELNANO) . IEEE, 2020, pp. 688–693
2020
Earlier work this paper cites.
A. Pal and R. Vidal, “A game theoretic analysis of additive adversarial attacks and defenses,” Advances in Neural Information Processing Systems , vol. 33, pp. 1345–1355, 2020
2020
Earlier work this paper cites.
J. Bose, G. Gidel, H. Berard, A. Cianflone, P. Vincent, S. Lacoste-Julien, and W. Hamilton, “Adversarial example games,” Advances in neural information processing systems , vol. 33, pp. 8921–8934, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
O. Koster, R. Kosman, and J. Visser, “A checklist for explainable ai in the insurance domain,” in International Conference on the Quality of Information and Communications Technology . Springer, 2021, pp. 446–456
2021
Earlier work this paper cites.
X. Chen, A. Salem, D. Chen, M. Backes, S. Ma, Q. Shen, Z. Wu, and Y. Zhang, “Badnl: Backdoor attacks against nlp models with semantic-preserving improvements,” in Proceedings of the 37th Annual Computer Security Applications Conference , 2021, pp. 554–569
2021
Earlier work this paper cites.
T. Zhai, Y. Li, Z. Zhang, B. Wu, Y. Jiang, and S.-T. Xia, “Backdoor attack against speaker verification,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 2560–2564
2021
Earlier work this paper cites.
E. B. Boukherouaa, M. G. Shabsigh, K. AlAjmi, J. Deodoro, A. Farias, E. S. Iskender, M. A. T. Mirestean, and R. Ravikumar, Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance . International Monetary Fund, 2021
2021
Cited alongside, same era.
H. A. Alsayadi, A. A. Abdelhamid, I. Hegazy, and Z. T. Fayed, “Non-diacritized arabic speech recognition based on cnn-lstm and attention-based models,” Journal of Intelligent & Fuzzy Systems , vol. 41, no. 6, pp. 6207–6219, 2021
2021
Cited alongside, same era.
J. Fan, B. Jiang, and Q. Sun, “Hoeffding’s inequality for general markov chains and its applications to statistical learning,” Journal of Machine Learning Research , vol. 22, no. 139, pp. 1–35, 2021
2021
Cited alongside, same era.
W. Zhang, S. Zhao, L. Liu, J. Li, X. Cheng, T. F. Zheng, and X. Hu, “Attack on practical speaker verification system using universal adversarial perturbations,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 2575–2579
Z. Khanjani, G. Watson, and V. P. Janeja, “Audio deepfakes: A survey,” Frontiers in Big Data , vol. 5, p. 1001063, 2023
2023
Later among the works it cites.
M. Masood, M. Nawaz, K. M. Malik, A. Javed, A. Irtaza, and H. Malik, “Deepfakes generation and detection: State-of-the-art, open challenges, countermeasures, and way forward,” Applied intelligence , vol. 53, no. 4, pp. 3974–4026, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. E. Cinà, K. Grosse, A. Demontis, S. Vascon, W. Zellinger, B. A. Moser, A. Oprea, B. Biggio, M. Pelillo, and F. Roli, “Wild patterns reloaded: A survey of machine learning security against training data poisoning,” ACM Computing Surveys , vol. 55, no. 13s, pp. 1–39, 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
K. Doan, Y. Lao, and P. Li, “Backdoor attack with imperceptible input and latent modification,” Advances in Neural Information Processing Systems , vol. 34, pp. 18 944–18 957, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. J. Bommarito II, D. M. Katz, and E. M. Detterman, “Lexnlp: Natural language processing and information extraction for legal and regulatory texts,” in Research handbook on big data law . Edward Elgar Publishing, 2021, pp. 216–227
2021
Cited alongside, same era.
Y. Li, Y. Jiang, Z. Li, and S.-T. Xia, “Backdoor learning: A survey,” IEEE Transactions on Neural Networks and Learning Systems , 2022
2022
Cited alongside, same era.
M. Goldblum, D. Tsipras, C. Xie, X. Chen, A. Schwarzschild, D. Song, A. Mądry, B. Li, and T. Goldstein, “Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 2, pp. 1563–1580, 2022
2022
Cited alongside, same era.
X. Sheng, Z. Han, P. Li, and X. Chang, “A survey on backdoor attack and defense in natural language processing,” in 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) . IEEE, 2022, pp. 809–820
2022
Cited alongside, same era.
S. Li, T. Dong, B. Z. H. Zhao, M. Xue, S. Du, and H. Zhu, “Backdoors against natural language processing: A review,” IEEE Security & Privacy , vol. 20, no. 5, pp. 50–59, 2022
2022
Cited alongside, same era.
D. Adesina, C.-C. Hsieh, Y. E. Sagduyu, and L. Qian, “Adversarial machine learning in wireless communications using rf data: A review,” IEEE Communications Surveys & Tutorials , vol. 25, no. 1, pp. 77–100, 2022
2022
Cited alongside, same era.
A. A. Ramadan and K. M. Ezzat, “Spoken digit recognition using machine and deep learning-based approaches,” in 2023 International Telecommunications Conference (ITC-Egypt) . IEEE, 2023, pp. 592–596
2023
Later among the works it cites.
E. Soremekun, S. Udeshi, and S. Chattopadhyay, “Towards backdoor attacks and defense in robust machine learning models,” Computers & Security , vol. 127, p. 103101, 2023
2023
Later among the works it cites.
H. Guo, X. Chen, J. Guo, L. Xiao, and Q. Yan, “Masterkey: Practical backdoor attack against speaker verification systems,” in Proceedings of the 29th Annual International Conference on Mobile Computing and Networking , 2023, pp. 1–15
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Ye, D. Yan, L. Dong, J. Deng, and S. Yu, “Stealthy backdoor attack against speaker recognition using phase-injection hidden trigger,” IEEE Signal Processing Letters , 2023
2023
Later among the works it cites.
S. Koffas, L. Pajola, S. Picek, and M. Conti, “Going in style: Audio backdoors through stylistic transformations,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
2023
Later among the works it cites.
O. Mahmoudi and M. F. Bouami, “Rnn and lstm models for arabic speech commands recognition using pytorch and gpu,” in International Conference on Artificial Intelligence & Industrial Applications . Springer, 2023, pp. 462–470
2023
Later among the works it cites.
X. Zou and W. Liu, “Generalization bounds for adversarial contrastive learning,” Journal of Machine Learning Research , vol. 24, no. 114, pp. 1–54, 2023
2023
Later among the works it cites.
K. Lu, M. C. Nguyen, X. Xu, and C. S. Foo, “On adversarial robustness of audio classifiers,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
A. Björklund, J. Mäkelä, and K. Puolamäki, “Slisemap: Supervised dimensionality reduction through local explanations,” Machine Learning , vol. 112, no. 1, pp. 1–43, 2023
2023
Later among the works it cites.
H. K. Surendrababu, “Model agnostic approach for nlp backdoor detection,” in 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) . IEEE, 2023, pp. 1–6
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Sawicki, M. Ganzha, and M. Paprzycki, “The state of the art of natural language processing—a systematic automated review of nlp literature using nlp techniques,” Data Intelligence , vol. 5, no. 3, pp. 707–749, 2023
2023
Later among the works it cites.
C. Barrett, B. Boyd, E. Bursztein, N. Carlini, B. Chen, J. Choi, A. R. Chowdhury, M. Christodorescu, A. Datta, S. Feizi, et al. , “Identifying and mitigating the security risks of generative ai,” Foundations and Trends® in Privacy and Security , vol. 6, no. 1, pp. 1–52, 2023
2023
Later among the works it cites.
S. Islam, H. Elmekki, A. Elsebai, J. Bentahar, N. Drawel, G. Rjoub, and W. Pedrycz, “A comprehensive survey on applications of transformers for deep learning tasks,” Expert Systems with Applications , p. 122666, 2023
2023
Later among the works it cites.
2024
Closest in time.
Y. Tang, L. Sun, and X. Xu, “Silenttrig: An imperceptible backdoor attack against speaker identification with hidden triggers,” Pattern Recognition Letters , vol. 177, pp. 103–109, 2024
2024
Closest in time.
X. Li, S. Wang, R. Huang, M. Gowda, and G. Kesidis, “Temporal-distributed backdoor attack against video based action recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 4, 2024, pp. 3199–3207
2024
Closest in time.
Y. Wan, Y. Qu, W. Ni, Y. Xiang, L. Gao, and E. Hossain, “Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey,” IEEE Communications Surveys & Tutorials , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
P. Dighe, Y. Su, S. Zheng, Y. Liu, V. Garg, X. Niu, and A. Tewfik, “Leveraging large language models for exploiting asr uncertainty,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 12 231–12 235
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
D. Cheng, S. Huang, and F. Wei, “Adapting large language models via reading comprehension,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=y886UXPEZ0
2024
Closest in time.
A. Trozze, T. Davies, and B. Kleinberg, “Large language models in cryptocurrency securities cases: can a gpt model meaningfully assist lawyers?” Artificial Intelligence and Law , pp. 1–47, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
B. Addad and K. Kapusta, “Homeopathic poisoning of rag systems,” in International Conference on Computer Safety, Reliability, and Security . Springer, 2024, pp. 358–364
2024
Closest in time.
2024
Closest in time.